Thrivacy

thrivacy

Table of Links Abstract and I. Introduction II. Expectations of Blockchain Accessibility III. Approach I: Maintain a Ledger Locally - Run a Full Node IV. Approach II: Query a Third-Party Ledger-Node-As-A-Service (NAAS) V. Approach III: Light Node - External Query & Local Verification VI. Concluding Remark and References II. EXPECTATIONS OF BLOCKCHAIN ACCESSIBILITY The process for a blockchain user to retrieve information from blockchains is similar to using any web service, such as browsing websites (retrieving information from a web server), checking e-mails (retrieving information from a mail server), etc. Therefore, the security expectations match the standard security triad: integrity, availability, and privacy. A. Integrity For blockchain accessibility, integrity means that the user can obtain the correct information on blockchains under the given security model. Much of the existing research focuses on the integrity of the blockchains from the perspective of full nodes, guaranteeing integrity if the user has access to a trusted (e.g. self-owned) full node. However, as more and more users turn to third-party service providers for information on blockchains, we notice that integrity may not be achieved if these service providers are untrusted. While some client protocols, such as Flyclient [10], ensure data integrity without much overhead, such techniques are not universally deployed on every blockchain or adopted by every service provider. B. Availability For blockchain accessibility, availability means that the user can freely access information on blockchains. Most incidents on third-party service providers revolve around availability [36] [39], and at least one vulnerability that creates a denial-of-service has been studied [35]. These cases of empirical evidence suggest that fully relying on a single third-party service may create availability issues. While it is industry practice to diversify and use several thirdparty services as backups of each other, it may also help if the user can employ a solution that does not rely on a single service but utilizes a group of nodes to ensure maximum availability. C. Privacy Privacy encompasses a broad range of cases available in this context: some users may want to conceal their queries from specific eavesdroppers to prevent frontrunning [17], while others may not want to be found using blockchains at all, with Bitcoin being illegal in some countries. These different concerns require drastically different security models that cannot be summarized in a nonspecific way. Therefore, we devote our discussion of privacy in this paper strictly to the problems specific to blockchain accessibility rather than general Internet anonymity needs: an untrusted server of the network should have no information about the users’ queries and the results. While existing Internet protocols such as HTTPS largely realize end-to-end confidentiality between the user and the trusted server, we note that if resolving the user’s query for information involves untrusted parties such as miners and third-party service providers, privacy is not trivially guaranteed and must be discussed on a case-by-case basis. For example, ConsenSys, the company that developed Infura and MetaMask, has claimed that when users use Infura as the default RPC provider in MetaMask, Infura will collect their IP address and Ethereum wallet address they send a transaction [15]. Authors:
(1) Zhongtang Luo, Purdue University (luo401@purdue.edu);
(2) Rohan Murukutla, Supra (r.murukutla@supraoracles.com);
(3) Aniket Kate, Purdue University / Supra (aniket@purdue.edu). This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Expectations of Blockchain Accessibility II. Expectations of Blockchain Accessibility III. Approach I: Maintain a Ledger Locally - Run a Full Node III. Approach I: Maintain a Ledger Locally - Run a Full Node IV. Approach II: Query a Third-Party Ledger-Node-As-A-Service (NAAS) IV. Approach II: Query a Third-Party Ledger-Node-As-A-Service (NAAS) V. Approach III: Light Node - External Query & Local Verification V. Approach III: Light Node - External Query & Local Verification VI. Concluding Remark and References VI. Concluding Remark and References II. EXPECTATIONS OF BLOCKCHAIN ACCESSIBILITY The process for a blockchain user to retrieve information from blockchains is similar to using any web service, such as browsing websites (retrieving information from a web server), checking e-mails (retrieving information from a mail server), etc. Therefore, the security expectations match the standard security triad: integrity, availability, and privacy. A. Integrity A. Integrity For blockchain accessibility, integrity means that the user can obtain the correct information on blockchains under the given security model. Much of the existing research focuses on the integrity of the blockchains from the perspective of full nodes, guaranteeing integrity if the user has access to a trusted (e.g. self-owned) full node. However, as more and more users turn to third-party service providers for information on blockchains, we notice that integrity may not be achieved if these service providers are untrusted. While some client protocols, such as Flyclient [10], ensure data integrity without much overhead, such techniques are not universally deployed on every blockchain or adopted by every service provider. B. Availability B. Availability For blockchain accessibility, availability means that the user can freely access information on blockchains. Most incidents on third-party service providers revolve around availability [36] [39], and at least one vulnerability that creates a denial-of-service has been studied [35]. These cases of empirical evidence suggest that fully relying on a single third-party service may create availability issues. While it is industry practice to diversify and use several thirdparty services as backups of each other, it may also help if the user can employ a solution that does not rely on a single service but utilizes a group of nodes to ensure maximum availability. C. Privacy C. Privacy Privacy encompasses a broad range of cases available in this context: some users may want to conceal their queries from specific eavesdroppers to prevent frontrunning [17], while others may not want to be found using blockchains at all, with Bitcoin being illegal in some countries. These different concerns require drastically different security models that cannot be summarized in a nonspecific way. Therefore, we devote our discussion of privacy in this paper strictly to the problems specific to blockchain accessibility rather than general Internet anonymity needs: an untrusted server of the network should have no information about the users’ queries and the results. While existing Internet protocols such as HTTPS largely realize end-to-end confidentiality between the user and the trusted server, we note that if resolving the user’s query for information involves untrusted parties such as miners and third-party service providers, privacy is not trivially guaranteed and must be discussed on a case-by-case basis. For example, ConsenSys, the company that developed Infura and MetaMask, has claimed that when users use Infura as the default RPC provider in MetaMask, Infura will collect their IP address and Ethereum wallet address they send a transaction [15]. Authors: (1) Zhongtang Luo, Purdue University (luo401@purdue.edu); (2) Rohan Murukutla, Supra (r.murukutla@supraoracles.com); (3) Aniket Kate, Purdue University / Supra (aniket@purdue.edu). Authors: Authors: (1) Zhongtang Luo, Purdue University (luo401@purdue.edu); (2) Rohan Murukutla, Supra (r.murukutla@supraoracles.com); (3) Aniket Kate, Purdue University / Supra (aniket@purdue.edu). This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Key Expectations for Blockchain Accessibility: Integrity, Availability, and Privacy

Harnessing Gen AI for Data Privacy

It is clear that navigating through any changes, whether they pertain to technology or legal aspects, can present challenges and are rarely straightforward.  Ekaterina Besedina, the Data & Measurement Lead-Expert, has vast experience in adapting to the new demands regarding user data privacy. Ekaterina is currently working on a project focusing on user privacy and feels passionate about it. She is really engrossed in her latest project. Ekaterina details that it all stemmed from the changing dynamics within digital advertising, especially with the recent rollout of user privacy initiatives by Apple and different regulatory shifts worldwide. These developments have paved the way for new advertising methods. It became apparent that a fresh approach was essential to adapt to this evolving landscape, and she is deeply involved in this process. No doubt user data privacy has become a serious issue these days. Ekaterina states that there are two main challenges to tackle when it comes to iOS advertisers. The first one involved adapting to and abiding by Apple's newly implemented privacy regulations. The second challenge was finding innovative strategies to navigate this uncharted territory while ensuring that their advertising efforts remained effective. Realizing the critical nature of the situation, Ekaterina delved into researching the consequences of these shifts and pinpointed the pressing necessity for advertisers to embrace the burgeoning iOS technology, SKAdNetwork. By employing this technology, advertisers can continue to gain insightful data on campaign effectiveness while upholding user privacy. So, this is how her project idea took shape - to create a robust framework mandating iOS advertisers to integrate SKAdNetwork into their strategies. This approach aims to ensure adherence to privacy regulations while maximizing advertising impact. By working closely with industry specialists and emphasizing forward-thinking, my project seeks to empower advertisers with the necessary resources to excel in this evolving digital advertising landscape. Surely, Ekaterina had questionable situations when introducing this project. One major hurdle she faced was the initial hesitancy and bewilderment among advertisers when confronted with the need to adjust to the new landscape of user privacy. It's natural that the sudden change in regulations and the requirement to revamp long-held advertising methods left many in a state of confusion and doubt. To overcome this pushback, she had to draw upon my technical knowledge, patience, and empathy. She dedicated a significant amount of time to enlightening advertisers about the significance of putting user privacy at the forefront and the essentiality of adopting new measurement methods to stay ahead of the competition. She had to promote open discussions, offer concise direction, and showcase the advantages of adjusting to the evolving environment. By directly addressing concerns and presenting tangible solutions, She managed to shift viewpoints gradually, fostering a smoother transition towards privacy-focused advertising methods. Ekaterina made some serious moves in measurement strategy that connects SKAdNetwork conversions with modeled conversions. Understanding the constraints of depending only on one approach, she spotted a chance to devise a more inclusive solution that gives advertisers a full picture of campaign effectiveness while honoring user privacy. By merging SKAdNetwork data with modeled conversions, she’s led the way in developing a technique that empowers advertisers to gain crucial insights into campaign performance while protecting user privacy. This method not only ensures adherence to changing regulations but also equips advertisers to make informed choices that lead to successful outcomes. By constantly testing and coming up with new ideas, she has shown that finding the right mix of privacy and performance in the online advertising realm is achievable. This sets the stage for a future that's not just sustainable but also rooted in ethics. Speaking about her role as the data and measurement lead in the organization, Ekaterina explains that she does her best to spearhead cutting-edge strategies that tackle the constantly shifting terrain of privacy regulations and technological advancements in the advertising sector. Her key features stem from my adeptness in maneuvering through these intricate obstacles, all the while safeguarding the efficiency and adherence to our advertising methodologies. At the same time, Ekaterina represents the #IamRemarkable initiative and serves as an ambassador for this organization. Ekaterina really finds this project very inspiring. She states that originally established at Google in 2016, the I Am Remarkable movement has since grown beyond Google's boundaries. Rmrkblty, a non-profit organization, was founded in August 2023 by Anna Wiener, the visionary behind the #IAmRemarkable program's inception at Google. Rmrkblty's core mission is to empower individuals to reach their fullest potential. The #IAmRemarkable program, the flagship offering of Rmrkblty, aims to boost self-confidence and self-promotion skills. She has led over 20 workshops for this program, catering to internal colleagues, external partners, and participants at International Women Techmakers conferences across various cities in Russia. To date, Ekaterina has successfully trained over 250 individuals through these workshops. Ekaterina notices that in our fast-changing world, people are still reluctant to make any shifts. She is sure that in order to be successful, it is better to welcome change with open arms instead of pushing back against it. By staying informed, staying adaptable, and actively searching for creative solutions, Ekaterina is sure that by following these approaches, she can effectively handle any obstacles and come out on the other side stronger and more resilient than ever. It is clear that navigating through any changes, whether they pertain to technology or legal aspects, can present challenges and are rarely straightforward.  Ekaterina Besedina, the Data & Measurement Lead-Expert, has vast experience in adapting to the new demands regarding user data privacy. Ekaterina is currently working on a project focusing on user privacy and feels passionate about it. She is really engrossed in her latest project. Ekaterina details that it all stemmed from the changing dynamics within digital advertising, especially with the recent rollout of user privacy initiatives by Apple and different regulatory shifts worldwide. These developments have paved the way for new advertising methods. It became apparent that a fresh approach was essential to adapt to this evolving landscape, and she is deeply involved in this process. No doubt user data privacy has become a serious issue these days . Ekaterina states that there are two main challenges to tackle when it comes to iOS advertisers. The first one involved adapting to and abiding by Apple's newly implemented privacy regulations. The second challenge was finding innovative strategies to navigate this uncharted territory while ensuring that their advertising efforts remained effective. No doubt user data privacy has become a serious issue these days Realizing the critical nature of the situation, Ekaterina delved into researching the consequences of these shifts and pinpointed the pressing necessity for advertisers to embrace the burgeoning iOS technology, SKAdNetwork. By employing this technology, advertisers can continue to gain insightful data on campaign effectiveness while upholding user privacy. So, this is how her project idea took shape - to create a robust framework mandating iOS advertisers to integrate SKAdNetwork into their strategies. This approach aims to ensure adherence to privacy regulations while maximizing advertising impact. By working closely with industry specialists and emphasizing forward-thinking, my project seeks to empower advertisers with the necessary resources to excel in this evolving digital advertising landscape. Surely, Ekaterina had questionable situations when introducing this project . One major hurdle she faced was the initial hesitancy and bewilderment among advertisers when confronted with the need to adjust to the new landscape of user privacy. It's natural that the sudden change in regulations and the requirement to revamp long-held advertising methods left many in a state of confusion and doubt. Surely, Ekaterina had questionable situations when introducing this project To overcome this pushback, she had to draw upon my technical knowledge, patience, and empathy. She dedicated a significant amount of time to enlightening advertisers about the significance of putting user privacy at the forefront and the essentiality of adopting new measurement methods to stay ahead of the competition. She had to promote open discussions, offer concise direction, and showcase the advantages of adjusting to the evolving environment. By directly addressing concerns and presenting tangible solutions, She managed to shift viewpoints gradually, fostering a smoother transition towards privacy-focused advertising methods. Ekaterina made some serious moves in measurement strategy that connects SKAdNetwork conversions with modeled conversions. Understanding the constraints of depending only on one approach, she spotted a chance to devise a more inclusive solution that gives advertisers a full picture of campaign effectiveness while honoring user privacy. Ekaterina made some serious moves in measurement strategy that connects SKAdNetwork conversions with modeled conversions. By merging SKAdNetwork data with modeled conversions, she’s led the way in developing a technique that empowers advertisers to gain crucial insights into campaign performance while protecting user privacy. This method not only ensures adherence to changing regulations but also equips advertisers to make informed choices that lead to successful outcomes. By constantly testing and coming up with new ideas, she has shown that finding the right mix of privacy and performance in the online advertising realm is achievable. This sets the stage for a future that's not just sustainable but also rooted in ethics. Speaking about her role as the data and measurement lead in the organization , Ekaterina explains that she does her best to spearhead cutting-edge strategies that tackle the constantly shifting terrain of privacy regulations and technological advancements in the advertising sector. Her key features stem from my adeptness in maneuvering through these intricate obstacles, all the while safeguarding the efficiency and adherence to our advertising methodologies. Speaking about her role as the data and measurement lead in the organization At the same time, Ekaterina represents the #IamRemarkable initiative and serves as an ambassador for this organization . Ekaterina really finds this project very inspiring. She states that originally established at Google in 2016, the I Am Remarkable movement has since grown beyond Google's boundaries. Rmrkblty, a non-profit organization, was founded in August 2023 by Anna Wiener, the visionary behind the #IAmRemarkable program's inception at Google. Rmrkblty's core mission is to empower individuals to reach their fullest potential. The #IAmRemarkable program, the flagship offering of Rmrkblty, aims to boost self-confidence and self-promotion skills. She has led over 20 workshops for this program, catering to internal colleagues, external partners, and participants at International Women Techmakers conferences across various cities in Russia. To date, Ekaterina has successfully trained over 250 individuals through these workshops. At the same time, Ekaterina represents the #IamRemarkable initiative and serves as an ambassador for this organization Ekaterina notices that in our fast-changing world, people are still reluctant to make any shifts . She is sure that in order to be successful, it is better to welcome change with open arms instead of pushing back against it. By staying informed, staying adaptable, and actively searching for creative solutions, Ekaterina is sure that by following these approaches, she can effectively handle any obstacles and come out on the other side stronger and more resilient than ever. Ekaterina notices that in our fast-changing world, people are still reluctant to make any shifts

Changes in User Data Privacy, How to Implement: Recommendations from Ekaterina Besedina

ব্যবহারকারীর ডেটা গোপনীয়তায় পরিবর্তন, কীভাবে প্রয়োগ করবেন: একাতেরিনা বেসেডিনার সুপারিশ

Änderungen im Datenschutz für Benutzer und deren Umsetzung: Empfehlungen von Ekaterina Besedina

Cambios en la privacidad de los datos del usuario, cómo implementarlos: recomendaciones de Ekaterina Besedina

Modifications de la confidentialité des données utilisateur, comment les mettre en œuvre : recommandations d'Ekaterina Besedina

उपयोगकर्ता डेटा गोपनीयता में परिवर्तन, कैसे लागू करें: एकातेरिना बेसेडिना की सिफारिशें

ユーザーデータプライバシーの変更とその実装方法: エカテリーナ・ベセディナからの推奨事項

사용자 데이터 개인 정보 보호의 변경 사항, 구현 방법: Ekaterina Besedina의 권장 사항

Mudanças na privacidade dos dados do usuário, como implementar: recomendações de Ekaterina Besedina

Изменения в конфиденциальности пользовательских данных, как реализовать: рекомендации Екатерины Бесединой

Kullanıcı Veri Gizliliğindeki Değişiklikler, Nasıl Uygulanmalı: Ekaterina Besedina'dan Öneriler

Những thay đổi về quyền riêng tư dữ liệu người dùng, cách thực hiện: Khuyến nghị từ Ekaterina Besedina

用户数据隐私的变化及其实施方法：Ekaterina Besedina 的建议

Recently, a decentralized blockchain network, the Internet Computer Protocol (ICP), unveiled its Verified Credentials (VCs) solution at the Digital Identity unConference Europe (DICE) 2024 in Zurich, Switzerland. This walletless infrastructure aims to facilitate efficient and trustworthy sharing of personal data while maintaining user privacy and control. Verified Credentials are digital representations of qualifications, achievements, or attributes that are cryptographically secured and portable. They are built on top of Internet Identity (II), a decentralized identity solution running on the Internet Computer blockchain. Internet Identity provides a robust authentication solution based on passkeys, which are more secure and convenient than traditional sign-in methods. One of the key advantages of Verified Credentials is that they allow users to assign identity attributes to their Internet Identity without revealing their unique identifier to different applications. This feature, called selective disclosure, enables users to verify specific information, such as their age, without disclosing their name or date of birth. By putting users in control of their data sharing, Verified Credentials enhance online privacy and security. Furthermore, Verified Credentials address the issue of dApp interoperability in a privacy-preserving manner. Internet Identity acts as a trusted intermediary between a Relying Party and an Issuer, using an alias to share credentials instead of the user's real principal. This approach prevents different applications from tracking users as they navigate the web. One notable application of Verified Credentials within the ICP ecosystem is the Proof of Unique Humanity (PoUH), implemented by the decentralized on-chain messaging app OpenChat and developed by Decide AI. PoUH links a credential to biometric data, such as facial, finger, or palm print recognition, ensuring that users are human and possess only one account on a platform. This measure aims to combat the activity of bots and foster more virtuous discourse on social media by preventing users from piloting multiple online accounts or manipulating token farming or airdrops. Final Thoughts The introduction of Verified Credentials by the Internet Computer Protocol represents a significant step towards enhancing online trust and privacy. By providing a walletless solution that allows users to selectively disclose their identity attributes, ICP empowers individuals to maintain control over their personal data while engaging with various applications and services. The implementation of Proof of Unique Humanity (PoUH) is particularly noteworthy, as it addresses the long-standing issue of bots and fake accounts manipulating public discourse on social media platforms. By linking credentials to biometric data, PoUH ensures that each user possesses only one account, thereby reducing the spread of disinformation and promoting more authentic interactions online. However, it is essential to consider the potential challenges and limitations associated with Verified Credentials and PoUH. For instance, the reliance on biometric data raises concerns about data privacy and security. ICP must implement robust measures to protect users' sensitive information and prevent unauthorized access or misuse. Additionally, while PoUH aims to combat the activity of bots and fake accounts, it is important to acknowledge that determined individuals may still find ways to circumvent the system. Continuous monitoring and improvement of the verification process will be necessary to maintain the effectiveness of PoUH. Overall, the introduction of Verified Credentials and Proof of Unique Humanity by the Internet Computer Protocol demonstrates a proactive approach to addressing the challenges of online trust and privacy. As the digital landscape continues to evolve, such innovations have the potential to reshape the way individuals interact with applications and services while maintaining control over their personal data. However, it is essential to remain vigilant and address any potential concerns to ensure the long-term success and adoption of these solutions. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Recently, a decentralized blockchain network, the Internet Computer Protocol ( ICP ), unveiled its Verified Credentials (VCs) solution at the Digital Identity unConference Europe (DICE) 2024 in Zurich, Switzerland. This walletless infrastructure aims to facilitate efficient and trustworthy sharing of personal data while maintaining user privacy and control. decentralized ICP Verified Credentials are digital representations of qualifications, achievements, or attributes that are cryptographically secured and portable. They are built on top of Internet Identity (II), a decentralized identity solution running on the Internet Computer blockchain. Internet Identity provides a robust authentication solution based on passkeys, which are more secure and convenient than traditional sign-in methods. One of the key advantages of Verified Credentials is that they allow users to assign identity attributes to their Internet Identity without revealing their unique identifier to different applications. This feature, called selective disclosure, enables users to verify specific information, such as their age, without disclosing their name or date of birth. By putting users in control of their data sharing, Verified Credentials enhance online privacy and security. Furthermore, Verified Credentials address the issue of dApp interoperability in a privacy-preserving manner. Internet Identity acts as a trusted intermediary between a Relying Party and an Issuer, using an alias to share credentials instead of the user's real principal. This approach prevents different applications from tracking users as they navigate the web. One notable application of Verified Credentials within the ICP ecosystem is the Proof of Unique Humanity (PoUH), implemented by the decentralized on-chain messaging app OpenChat and developed by Decide AI. PoUH links a credential to biometric data, such as facial, finger, or palm print recognition, ensuring that users are human and possess only one account on a platform. This measure aims to combat the activity of bots and foster more virtuous discourse on social media by preventing users from piloting multiple online accounts or manipulating token farming or airdrops. Final Thoughts The introduction of Verified Credentials by the Internet Computer Protocol represents a significant step towards enhancing online trust and privacy. By providing a walletless solution that allows users to selectively disclose their identity attributes, ICP empowers individuals to maintain control over their personal data while engaging with various applications and services. The implementation of Proof of Unique Humanity (PoUH) is particularly noteworthy, as it addresses the long-standing issue of bots and fake accounts manipulating public discourse on social media platforms. By linking credentials to biometric data, PoUH ensures that each user possesses only one account, thereby reducing the spread of disinformation and promoting more authentic interactions online. However, it is essential to consider the potential challenges and limitations associated with Verified Credentials and PoUH. For instance, the reliance on biometric data raises concerns about data privacy and security. ICP must implement robust measures to protect users' sensitive information and prevent unauthorized access or misuse. Additionally, while PoUH aims to combat the activity of bots and fake accounts, it is important to acknowledge that determined individuals may still find ways to circumvent the system. Continuous monitoring and improvement of the verification process will be necessary to maintain the effectiveness of PoUH. Overall, the introduction of Verified Credentials and Proof of Unique Humanity by the Internet Computer Protocol demonstrates a proactive approach to addressing the challenges of online trust and privacy. As the digital landscape continues to evolve, such innovations have the potential to reshape the way individuals interact with applications and services while maintaining control over their personal data. However, it is essential to remain vigilant and address any potential concerns to ensure the long-term success and adoption of these solutions. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program business blogging program

How ICP's Walletless Solution Enhances Online Trust and Privacy

কিভাবে ICP এর Walletless সমাধান অনলাইন বিশ্বাস এবং গোপনীয়তা বাড়ায়

Wie die Walletless-Lösung von ICP das Online-Vertrauen und den Datenschutz verbessert

Cómo la solución Walletless de ICP mejora la confianza y la privacidad en línea

Comment la solution sans portefeuille d'ICP améliore la confiance et la confidentialité en ligne

आईसीपी का वॉलेटलेस समाधान ऑनलाइन विश्वास और गोपनीयता को कैसे बढ़ाता है

ICP のウォレットレス ソリューションがオンラインの信頼性とプライバシーを強化する方法

ICP의 Walletless 솔루션이 온라인 신뢰와 개인정보 보호를 강화하는 방법

Como a solução sem carteira da ICP aumenta a confiança e a privacidade online

Как решение ICP без кошелька повышает доверие и конфиденциальность в Интернете

ICP'nin Cüzdansız Çözümü Çevrimiçi Güveni ve Gizliliği Nasıl Artırıyor?

Giải pháp không cần ví của ICP nâng cao sự tin cậy và quyền riêng tư trực tuyến như thế nào

ICP 的无钱包解决方案如何增强在线信任和隐私

Authors:
(1) Ángel Merino, Department of Telematic Engineering Universidad Carlos III de Madrid {angel.merino@uc3m.es};
(2) José González-Cabañas, UC3M-Santander Big Data Institute {jose.gonzalez.cabanas@uc3m.es}
(3) Ángel Cuevas, Department of Telematic Engineering Universidad Carlos III de Madrid & UC3M-Santander Big Data Institute {acrumin@it.uc3m.es};
(4) Rubén Cuevas, Department of Telematic Engineering Universidad Carlos III de Madrid & UC3M-Santander Big Data Institute {rcuevas@it.uc3m.es}. Table of Links Abstract and Introduction LinkedIn Advertising Platform Background Dataset Methodology User’s Uniqueness on LinkedIn Nanotargeting proof of concept Discussion Related work Ethics and legal considerations Conclusions, Acknowledgments, and References Appendix A Country distribution of users in our data sample Our dataset contains samples from 58 different countries, but a few countries constitute most of our dataset, especially the United States. Table 4 shows the breakdown of the number of users per country in our dataset. About 75% of the users in our dataset are from the United States. We acknowledge the fact that this circumstance in our data may lead to some biases in the results of our model and, therefore, to the estimation of N. However, the fact that the proof of concept experiment was targeting users in a different country than the US and the obtained results are aligned with the model outcome makes us confident that the potential bias (if any) may be slight. As discussed in the paper, our intuition is that our model serves as an upper bound for the number of skills needed to make a user unique. This may be because the United States is one of the countries including more LinkedIn users. Therefore, it seems reasonable to estimate that, in many cases, it will be easier to re-identify users reporting a different location than the US. B Model fitting in the other considered scenarios We apply our methodology to four different scenarios summarized in section 4.3. This appendix shows the model fitting for the other three scenarios not shown in the paper body referred to as (ii) Sk_LP (Figure 7), we only use the least popular professional skills; (iii) Lo_R (Figure 8), we use the location and professional skills selected at random; (iv) Lo_LP (Figure 9), we use the location and the least popular skills. Table 1 shows the R2 values for all the line fittings. C LinkedIn dashboard report for the proof of concept experiment ad campaigns Some of the results reported in Table 3 were extracted from the LinkedIn Campaign Manager report for the different advertising campaigns delivered. Figure 10 shows a snapshot of the information reported by LinkedIn for the 15 ad campaigns executed in our proof of concept experiment. It shows the ID of the campaign, the budget spent, the number of delivered ad impressions, and the number of clicks received. While we acknowledge that the report may not be 100% accurate, the fact that impressions counted by the targeted individuals in our experiment always match the number of impressions reported by LinkedIn leads us to think that not only that the targeted users the only ones have received their corresponding ad, but also that the LinkedIn count is accurate, at least for audiences close to 1 user. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Ángel Merino, Department of Telematic Engineering Universidad Carlos III de Madrid {angel.merino@uc3m.es}; (2) José González-Cabañas, UC3M-Santander Big Data Institute {jose.gonzalez.cabanas@uc3m.es} (3) Ángel Cuevas, Department of Telematic Engineering Universidad Carlos III de Madrid & UC3M-Santander Big Data Institute {acrumin@it.uc3m.es}; (4) Rubén Cuevas, Department of Telematic Engineering Universidad Carlos III de Madrid & UC3M-Santander Big Data Institute {rcuevas@it.uc3m.es}. Authors: Authors: (1) Ángel Merino, Department of Telematic Engineering Universidad Carlos III de Madrid {angel.merino@uc3m.es}; (2) José González-Cabañas, UC3M-Santander Big Data Institute {jose.gonzalez.cabanas@uc3m.es} (3) Ángel Cuevas, Department of Telematic Engineering Universidad Carlos III de Madrid & UC3M-Santander Big Data Institute {acrumin@it.uc3m.es}; (4) Rubén Cuevas, Department of Telematic Engineering Universidad Carlos III de Madrid & UC3M-Santander Big Data Institute {rcuevas@it.uc3m.es}. Table of Links Abstract and Introduction Abstract and Introduction LinkedIn Advertising Platform Background LinkedIn Advertising Platform Background Dataset Dataset Methodology Methodology User’s Uniqueness on LinkedIn User’s Uniqueness on LinkedIn Nanotargeting proof of concept Nanotargeting proof of concept Discussion Discussion Related work Related work Ethics and legal considerations Ethics and legal considerations Conclusions, Acknowledgments, and References Conclusions, Acknowledgments, and References Appendix Appendix A Country distribution of users in our data sample Our dataset contains samples from 58 different countries, but a few countries constitute most of our dataset, especially the United States. Table 4 shows the breakdown of the number of users per country in our dataset. About 75% of the users in our dataset are from the United States. We acknowledge the fact that this circumstance in our data may lead to some biases in the results of our model and, therefore, to the estimation of N. However, the fact that the proof of concept experiment was targeting users in a different country than the US and the obtained results are aligned with the model outcome makes us confident that the potential bias (if any) may be slight. As discussed in the paper, our intuition is that our model serves as an upper bound for the number of skills needed to make a user unique. This may be because the United States is one of the countries including more LinkedIn users. Therefore, it seems reasonable to estimate that, in many cases, it will be easier to re-identify users reporting a different location than the US. B Model fitting in the other considered scenarios We apply our methodology to four different scenarios summarized in section 4.3. This appendix shows the model fitting for the other three scenarios not shown in the paper body referred to as (ii) Sk_LP (Figure 7), we only use the least popular professional skills; (iii) Lo_R (Figure 8), we use the location and professional skills selected at random; (iv) Lo_LP (Figure 9), we use the location and the least popular skills. Table 1 shows the R2 values for all the line fittings. C LinkedIn dashboard report for the proof of concept experiment ad campaigns Some of the results reported in Table 3 were extracted from the LinkedIn Campaign Manager report for the different advertising campaigns delivered. Figure 10 shows a snapshot of the information reported by LinkedIn for the 15 ad campaigns executed in our proof of concept experiment. It shows the ID of the campaign, the budget spent, the number of delivered ad impressions, and the number of clicks received. While we acknowledge that the report may not be 100% accurate, the fact that impressions counted by the targeted individuals in our experiment always match the number of impressions reported by LinkedIn leads us to think that not only that the targeted users the only ones have received their corresponding ad, but also that the LinkedIn count is accurate, at least for audiences close to 1 user. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

GDPR and Non-PII Data: Addressing Privacy Vulnerabilities in Nanotargeting

Nanotargeting Risks: The Urgent Need for Better Privacy Protections

Authors:
(1) Ángel Merino, Department of Telematic Engineering Universidad Carlos III de Madrid {angel.merino@uc3m.es};
(2) José González-Cabañas, UC3M-Santander Big Data Institute {jose.gonzalez.cabanas@uc3m.es}
(3) Ángel Cuevas, Department of Telematic Engineering Universidad Carlos III de Madrid & UC3M-Santander Big Data Institute {acrumin@it.uc3m.es};
(4) Rubén Cuevas, Department of Telematic Engineering Universidad Carlos III de Madrid & UC3M-Santander Big Data Institute {rcuevas@it.uc3m.es}. Table of Links Abstract and Introduction LinkedIn Advertising Platform Background Dataset Methodology User’s Uniqueness on LinkedIn Nanotargeting proof of concept Discussion Related work Ethics and legal considerations Conclusions, Acknowledgments, and References Appendix 2 LinkedIn Advertising Platform Background One of LinkedIn’s primary sources of revenue is online advertising [9]. Advertisers use the LinkedIn Campaign Manager [10] to define their target audience by selecting attributes such as location, gender, age, professional skills, etc. Once the target audience is defined, LinkedIn delivers the ads to users whose profile matches the selected attributes. For example, an advertiser could target users in Germany between 18 and 25 who are skilled in Python AND R. LinkedIn reports the estimated number of users that match the target audience defined through the dashboard, referred to as Target Audience Size. This information permits advertisers to know the potential reach of their ad campaign. In our work, we leverage this functionality and use specific HTTP queries to systematically collect the size of thousands of audiences. An essential feature of the LinkedIn Campaign Manager is that it allows us to narrow the audience size by combining non-PII attributes using the AND operator. Moreover, the information of users (location, skills, experience, education, etc.) is publicly available in their profile for anyone registered on LinkedIn. We have explored whether it is possible to customize the access to a user profile (e.g., to limit the access only to direct contacts), and it was not possible. It is important to note that LinkedIn is a professional social network where users are willing to expose their profiles publicly. This may be why LinkedIn does not consider offering users profile visibility customization options relevant. LinkedIn seems to consider that revealing the actual value of small audiences may be problematic and does not report the actual audience size for any audience formed by less than 300 users. This is a positive privacy-preserving measure, a standard among social media platforms with a similar business model. For instance, Facebook and TikTok have defined a lower bound of 1000 users. Moreover, we found a LinkedIn document including guidelines for advertisers where they specifically state: "The minimum audience size required to run an advertising campaign is 300 members" [11]. We understand this as a policy imposed by LinkedIn on ad campaigns. This measure would impede advertisers (attackers) from running nanotargeting campaigns to target a specific individual. Unfortunately, this paper demonstrates that this policy is not effectively enforced since it is possible to nanotarget LinkedIn users. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Ángel Merino, Department of Telematic Engineering Universidad Carlos III de Madrid {angel.merino@uc3m.es}; (2) José González-Cabañas, UC3M-Santander Big Data Institute {jose.gonzalez.cabanas@uc3m.es} (3) Ángel Cuevas, Department of Telematic Engineering Universidad Carlos III de Madrid & UC3M-Santander Big Data Institute {acrumin@it.uc3m.es}; (4) Rubén Cuevas, Department of Telematic Engineering Universidad Carlos III de Madrid & UC3M-Santander Big Data Institute {rcuevas@it.uc3m.es}. Authors: Authors: (1) Ángel Merino, Department of Telematic Engineering Universidad Carlos III de Madrid {angel.merino@uc3m.es}; (2) José González-Cabañas, UC3M-Santander Big Data Institute {jose.gonzalez.cabanas@uc3m.es} (3) Ángel Cuevas, Department of Telematic Engineering Universidad Carlos III de Madrid & UC3M-Santander Big Data Institute {acrumin@it.uc3m.es}; (4) Rubén Cuevas, Department of Telematic Engineering Universidad Carlos III de Madrid & UC3M-Santander Big Data Institute {rcuevas@it.uc3m.es}. Table of Links Abstract and Introduction Abstract and Introduction LinkedIn Advertising Platform Background LinkedIn Advertising Platform Background Dataset Dataset Methodology Methodology User’s Uniqueness on LinkedIn User’s Uniqueness on LinkedIn Nanotargeting proof of concept Nanotargeting proof of concept Discussion Discussion Related work Related work Ethics and legal considerations Ethics and legal considerations Conclusions, Acknowledgments, and References Conclusions, Acknowledgments, and References Appendix Appendix 2 LinkedIn Advertising Platform Background One of LinkedIn’s primary sources of revenue is online advertising [9]. Advertisers use the LinkedIn Campaign Manager [10] to define their target audience by selecting attributes such as location, gender, age, professional skills, etc. Once the target audience is defined, LinkedIn delivers the ads to users whose profile matches the selected attributes. For example, an advertiser could target users in Germany between 18 and 25 who are skilled in Python AND R. LinkedIn reports the estimated number of users that match the target audience defined through the dashboard, referred to as Target Audience Size. This information permits advertisers to know the potential reach of their ad campaign. In our work, we leverage this functionality and use specific HTTP queries to systematically collect the size of thousands of audiences. An essential feature of the LinkedIn Campaign Manager is that it allows us to narrow the audience size by combining non-PII attributes using the AND operator. Moreover, the information of users (location, skills, experience, education, etc.) is publicly available in their profile for anyone registered on LinkedIn. We have explored whether it is possible to customize the access to a user profile (e.g., to limit the access only to direct contacts), and it was not possible. It is important to note that LinkedIn is a professional social network where users are willing to expose their profiles publicly. This may be why LinkedIn does not consider offering users profile visibility customization options relevant. LinkedIn seems to consider that revealing the actual value of small audiences may be problematic and does not report the actual audience size for any audience formed by less than 300 users. This is a positive privacy-preserving measure, a standard among social media platforms with a similar business model. For instance, Facebook and TikTok have defined a lower bound of 1000 users. Moreover, we found a LinkedIn document including guidelines for advertisers where they specifically state: "The minimum audience size required to run an advertising campaign is 300 members" [11]. We understand this as a policy imposed by LinkedIn on ad campaigns. This measure would impede advertisers (attackers) from running nanotargeting campaigns to target a specific individual. Unfortunately, this paper demonstrates that this policy is not effectively enforced since it is possible to nanotarget LinkedIn users. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Privacy and Targeting on LinkedIn: Insights into the Campaign Manager

The Noonification: Navigating Privacy in the Age of Big Compute (5/30/2024)

Navigating Privacy in the Age of Big Compute

বিগ কম্পিউটের যুগে গোপনীয়তা নেভিগেট করা

Datenschutz im Zeitalter großer Computer

Navegando por la privacidad en la era de la gran informática

Naviguer dans la confidentialité à l’ère du Big Compute

बड़े कंप्यूटर के युग में गोपनीयता का प्रबंधन

ビッグコンピューティング時代のプライバシーのあり方

빅 컴퓨팅 시대의 개인 정보 보호 탐색

Navegando pela privacidade na era da grande computação

Обеспечение конфиденциальности в эпоху больших вычислений

Büyük Bilgi İşlem Çağında Gizliliğe Yönelmek

Điều hướng quyền riêng tư trong thời đại điện toán lớn

大计算时代的隐私保护

With an alarming 46 percent statistical concern about access to treatment in the healthcare system around the world, AI is seeking to democratize and simplify the process of diagnosis and patient treatment. With companies like Google, GE Healthcare, Siemens Healthineers, and many other leading AI startups, these AI dreams are achieving more feasibility. However, how will AI’s health solution be future-proof if privacy concerns and data breaches are still lurking? Overview of AI advancements GE Healthcare has been able to develop a technology that assists with ultra-sound imaging, and high-level accurate scans like PET, MRI, and CT. it is also in the U.S. Food and Drug Administration-approved AI medical devices having 58 out of 692 entries. Siemens Healthineers claims to have more than 80 AI-based healthcare solutions with 40 hardware devices approved lately. The focus of this company is the algorithmic mechanism that automates the post-processing of images and helps radiologists handle heavy workflows and routine tasks. According to an article on VKTR by Neil Savage, “it includes modules for chest CT, which can highlight lung nodules and estimate tumor burden; brain MR, which divides the brain into different segments and makes volumetric measurements” A host of other startups are promising in harnessing the potential of the intelligent models in addressing the human problems with the current healthcare system. Can we trust AI with our healthcare data? AI by default makes decisions and recommendations based on the data it has been trained with and all forms have an underlying principle of large datasets in finding patterns. This leads us to our first question on how the benefits be balanced with the disadvantages in utilizing people’s healthcare data for training and decisions. The concerns are in two categories. The first concern is the privacy of the data used to train these models, second is the model’s response based on the input of the patient’s data in the working process—or decision-making process of the model. AI’s advancements in the healthcare venture have received ovations with its impressive abilities ranging from medical evaluations, automation, chest x-rays, smart wearables, medical diagnosis, imaging processing, and robotic surgery—which has been touted as a hypothetical possibility with recent innovations and on a higher level rather than the current level with tiny incisions. In Health IT security’s words, “Artificial intelligence (AI) in healthcare has the potential to revolutionize clinical research and care delivery. AI can be used to sift through massive databases and may have applications in mental healthcare and cancer detection.” Over time, the medical profession has prioritized the oath of secrecy in the medical process of diagnosis, treatments, and prescriptions. However, a data-oriented system solving the measure of traditional problems while maintaining the status quo remains a vital cornerstone to the utility of this innovation. Definitive Healthcare has identified about 80 percent of health data breaches due to hacking or IT incidents. Therefore, there’s still a lot to be addressed when fears abound about the possibility of weaponized models, poisoning, and other sort of invasive possibilities on the technology that was originally meant to assist and automate tasks. This compounds the problems and leaves gaps as the healthcare AI application accelerates at an unprecedented pace. Several approaches may mitigate problems and tie up the loose ends which will be detailed in succeeding paragraphs. Looking forward: The turning point and AI promises A technical approach such as anonymizing, encryption, and creating privacy-oriented algorithms in storing and retrieval of health care data. An example is blockchain technology detailed in this paper by the NLM. A second way can be developing legal frameworks in protecting the medical records of individuals and according to Valentin Kuzmenco, in an article on Hyperight, “We need robust legal frameworks to safeguard our privacy in the digital age. Stringent data protection laws like GDPR and CCPA offer a starting point,… protecting our data from unauthorized access and misuse, regardless of geographical borders.” There’s a longtime perpetual concern of big tech with people’s data and their collateral cost of internet surfing, and recommendation algorithms. Patients can be educated on best privacy practices, and providing consent forms—because not all forms of medical data may be deemed private Thirdly, there is ongoing research in addressing the underlying concerns and a continual progression will address technological challenges with data privacy and automative algorithms. Being on the lookout while supervising automation for progressive steps will go a long way. In conclusion, the impact of AI in the healthcare industry is promising and has lots of potential to mitigate current and future healthcare problems and automate tasks to lower costs and democratize healthcare. However, best security practices, compliance regulatory frameworks, and continual research would enhance a futuristic possibility with innovations and advancements. With an alarming 46 percent statistical concern about access to treatment in the healthcare system around the world, AI is seeking to democratize and simplify the process of diagnosis and patient treatment. With companies like Google , GE Healthcare , Siemens Healthineers , and many other leading AI startups, these AI dreams are achieving more feasibility. 46 percent statistical concern Google GE Healthcare Siemens Healthineers However, how will AI’s health solution be future-proof if privacy concerns and data breaches are still lurking? Overview of AI advancements Overview of AI advancements GE Healthcare has been able to develop a technology that assists with ultra-sound imaging, and high-level accurate scans like PET, MRI, and CT. it is also in the U.S. Food and Drug Administration-approved AI medical devices having 58 out of 692 entries. GE Healthcare Siemens Healthineers claims to have more than 80 AI-based healthcare solutions with 40 hardware devices approved lately. The focus of this company is the algorithmic mechanism that automates the post-processing of images and helps radiologists handle heavy workflows and routine tasks. Siemens Healthineers According to an article on VKTR by Neil Savage, “it includes modules for chest CT, which can highlight lung nodules and estimate tumor burden; brain MR, which divides the brain into different segments and makes volumetric measurements” VKTR A host of other startups are promising in harnessing the potential of the intelligent models in addressing the human problems with the current healthcare system. host of other startups Can we trust AI with our healthcare data? Can we trust AI with our healthcare data? AI by default makes decisions and recommendations based on the data it has been trained with and all forms have an underlying principle of large datasets in finding patterns. This leads us to our first question on how the benefits be balanced with the disadvantages in utilizing people’s healthcare data for training and decisions. The concerns are in two categories. The first concern is the privacy of the data used to train these models, second is the model’s response based on the input of the patient’s data in the working process—or decision-making process of the model. AI’s advancements in the healthcare venture have received ovations with its impressive abilities ranging from medical evaluations, automation, chest x-rays, smart wearables, medical diagnosis, imaging processing, and robotic surgery—which has been touted as a hypothetical possibility with recent innovations and on a higher level rather than the current level with tiny incisions. In Health IT security’s words, “Artificial intelligence (AI) in healthcare has the potential to revolutionize clinical research and care delivery. AI can be used to sift through massive databases and may have applications in mental healthcare and cancer detection.” Over time, the medical profession has prioritized the oath of secrecy in the medical process of diagnosis, treatments, and prescriptions. However, a data-oriented system solving the measure of traditional problems while maintaining the status quo remains a vital cornerstone to the utility of this innovation. Definitive Healthcare has identified about 80 percent of health data breaches due to hacking or IT incidents. Therefore, there’s still a lot to be addressed when fears abound about the possibility of weaponized models, poisoning, and other sort of invasive possibilities on the technology that was originally meant to assist and automate tasks. Definitive identified invasive This compounds the problems and leaves gaps as the healthcare AI application accelerates at an unprecedented pace. Several approaches may mitigate problems and tie up the loose ends which will be detailed in succeeding paragraphs. Looking forward: The turning point and AI promises Looking forward: The turning point and AI promises A technical approach such as anonymizing, encryption, and creating privacy-oriented algorithms in storing and retrieval of health care data. An example is blockchain technology detailed in this paper by the NLM. paper A second way can be developing legal frameworks in protecting the medical records of individuals and according to Valentin Kuzmenco, in an article on Hyperight, “We need robust legal frameworks to safeguard our privacy in the digital age. Stringent data protection laws like GDPR and CCPA offer a starting point,… protecting our data from unauthorized access and misuse, regardless of geographical borders.” GDPR and CCPA There’s a longtime perpetual concern of big tech with people’s data and their collateral cost of internet surfing, and recommendation algorithms. Patients can be educated on best privacy practices, and providing consent forms—because not all forms of medical data may be deemed private Thirdly, there is ongoing research in addressing the underlying concerns and a continual progression will address technological challenges with data privacy and automative algorithms. Being on the lookout while supervising automation for progressive steps will go a long way. In conclusion, the impact of AI in the healthcare industry is promising and has lots of potential to mitigate current and future healthcare problems and automate tasks to lower costs and democratize healthcare. However, best security practices, compliance regulatory frameworks, and continual research would enhance a futuristic possibility with innovations and advancements.

As Healthcare AI Advances, How Do we Balance the Benefits With Privacy Concerns?

In light of the extensive privacy debates of recent years, global companies are formulating new ways to approach privacy and its implementation in business. From May 1st, 2024, Apple is introducing new privacy changes – a privacy policy that will require all developers to attach a privacy manifest file, describing the data that is collected by the app and providing valid reasons for the use of its API. So, What Are Privacy Manifests? Essentially, a privacy manifest is a new-age tool that can provide more clarity for users and developers. A tool that can help identify your app’s dependency's privacy practices. It is a file that allows apps or third-party SDK developers to share information about the way they approach privacy. It comes in the form of a `plist` file, recording the data that is collected in the app. It shows the type of data an SDK collects, how the data is used, any links to users, and if this information is used for tracking (Apple also has an App Tracking Transparency policy). A privacy manifest can also record data collected by third-party SDKs. How does it provide detailed reasoning? For each API that requires justification for its use, the file lists the reasons for the use. The Deadlines and Requirements Starting with March 13th, Apple has already contacted app developers, urging them to implement privacy manifests in the updated versions. That was the first timestamp of this process. Now, from May 1st, all new and updated apps headed to the app store will require a privacy manifest to be approved. This includes all IOS-based apps, including tvOS, iPad OS, and even the latest VisionOS – all of which can be found in the App Store. This also includes all new or updated apps that will have a new third-party SDK (or the common ones too will become a breeze for developers. Benefits for All These privacy manifests are a great tool that are bound to change the global developers’ approach to privacy for the better, which will inevitably impact user experience as a result. Privacy manifests can help developers communicate and adhere to rules, creating more transparent and safe apps for the AppStore. Moreover, now that all third-party SDKs from a single app can be summarized in one privacy manifest report, the task of creating accurate Privacy Nutrition Labels. Creating the Privacy Manifest File You can start the process straight away – all you have to do is open Xcode 15 and select: File -> New -> File Then go to IOS, and select App Privacy from the Resource section in the panel. Press Next.


Choose your destination Folder – this is where the file will be saved. It will be named `PrivacyInfo.xcprivacy` by default. If you need your third-party SDK to be shared as a Static Library, you can use Xcode to bundle resources, including the privacy manifest file. Create a framework target in Xcode, select "Static Library," as its Mach-O type, and add the privacy manifest and other resources to the bundle. Done. 3. After saving, an Xcode Editor window will pop up on your screen for the file you saved above. Select the App Privacy Configuration entry, and add it by clicking the plus button on the right. Now, these are the steps for adding new Privacy Accessed API Type entries and new Privacy Accessed API Reasons. Continuing from step 3, click on the Privacy Accessed API Types entry. Here, you need to select the Item 0 entry and click the ‘add’ button.


Click on Privacy Accessed API Type, and set the entry value to File Timestamp.


Click on Item 0 once more to add Privacy Accessed API Reasons.


Select Item 0 under Privacy API Reasons, and set its value to what you need. From here on, to add new entries, all you need to do is repeat the 4 steps described above, and each time, add new values. In case you need extra Reasons/Categories for your app because of other libraries and plug-ins, those need to be in the manifest too. Time for a privacy report Luckily, Xcode can help you gather all the declared data from the privacy manifests into one single report, which comes incredibly useful in the case of a review. It comes as a PDF and looks something like the Privacy Nutrition Labels we mentioned before. To generate one, select Product, and then Archive, and select Generate Privacy Report. To Sum Up Now, there are numerous APIs for required reasons and collected data types that developers have to record according to Apple’s new Privacy Manifests policy, but we can get into those in another article. Overall, this initiative is great – it will allow the App Store to conduct a more thorough selection process in terms of privacy and safety, users will have a safer and more transparent experience, and developers… Well, it will take some getting used to. But it’s all for the sake of safety. In light of the extensive privacy debates of recent years, global companies are formulating new ways to approach privacy and its implementation in business. From May 1st, 2024, Apple is introducing new privacy changes – a privacy policy that will require all developers to attach a privacy manifest file, describing the data that is collected by the app and providing valid reasons for the use of its API. So, What Are Privacy Manifests? So, What Are Privacy Manifests? Essentially, a privacy manifest is a new-age tool that can provide more clarity for users and developers. A tool that can help identify your app’s dependency's privacy practices. It is a file that allows apps or third-party SDK developers to share information about the way they approach privacy. It comes in the form of a `plist` file, recording the data that is collected in the app. It shows the type of data an SDK collects, how the data is used, any links to users, and if this information is used for tracking (Apple also has an App Tracking Transparency policy ). A privacy manifest can also record data collected by third-party SDKs. How does it provide detailed reasoning? For each API that requires justification for its use, the file lists the reasons for the use. App Tracking Transparency policy App Tracking Transparency policy The Deadlines and Requirements The Deadlines and Requirements Starting with March 13th, Apple has already contacted app developers, urging them to implement privacy manifests in the updated versions. That was the first timestamp of this process. Now, from May 1st, all new and updated apps headed to the app store will require a privacy manifest to be approved. This includes all IOS-based apps, including tvOS, iPad OS, and even the latest VisionOS – all of which can be found in the App Store. This also includes all new or updated apps that will have a new third-party SDK (or the common ones too will become a breeze for developers. Benefits for All Benefits for All These privacy manifests are a great tool that are bound to change the global developers’ approach to privacy for the better, which will inevitably impact user experience as a result. Privacy manifests can help developers communicate and adhere to rules, creating more transparent and safe apps for the AppStore. Moreover, now that all third-party SDKs from a single app can be summarized in one privacy manifest report, the task of creating accurate Privacy Nutrition Labels . Privacy Nutrition Labels Privacy Nutrition Labels Creating the Privacy Manifest File Creating the Privacy Manifest File You can start the process straight away – all you have to do is open Xcode 15 and select: File -> New -> File Then go to IOS, and select App Privacy from the Resource section in the panel. Press Next. Choose your destination Folder – this is where the file will be saved. It will be named `PrivacyInfo.xcprivacy` by default. Then go to IOS, and select App Privacy from the Resource section in the panel. Press Next. Then go to IOS, and select App Privacy from the Resource section in the panel. Press Next. Choose your destination Folder – this is where the file will be saved. It will be named `PrivacyInfo.xcprivacy` by default. Choose your destination Folder – this is where the file will be saved. It will be named `PrivacyInfo.xcprivacy` by default. If you need your third-party SDK to be shared as a Static Library, you can use Xcode to bundle resources, including the privacy manifest file. Create a framework target in Xcode, select "Static Library," as its Mach-O type, and add the privacy manifest and other resources to the bundle. Done. If you need your third-party SDK to be shared as a Static Library, you can use Xcode to bundle resources, including the privacy manifest file. Create a framework target in Xcode, select "Static Library," as its Mach-O type, and add the privacy manifest and other resources to the bundle. Done. 3. After saving, an Xcode Editor window will pop up on your screen for the file you saved above. Select the App Privacy Configuration entry, and add it by clicking the plus button on the right. Now, these are the steps for adding new Privacy Accessed API Type entries and new Privacy Accessed API Reasons. Continuing from step 3, click on the Privacy Accessed API Types entry. Here, you need to select the Item 0 entry and click the ‘add’ button. Click on Privacy Accessed API Type, and set the entry value to File Timestamp. Click on Item 0 once more to add Privacy Accessed API Reasons. Select Item 0 under Privacy API Reasons, and set its value to what you need. Continuing from step 3, click on the Privacy Accessed API Types entry. Here, you need to select the Item 0 entry and click the ‘add’ button. Continuing from step 3, click on the Privacy Accessed API Types entry. Here, you need to select the Item 0 entry and click the ‘add’ button. Click on Privacy Accessed API Type, and set the entry value to File Timestamp. Click on Privacy Accessed API Type, and set the entry value to File Timestamp. Click on Item 0 once more to add Privacy Accessed API Reasons. Click on Item 0 once more to add Privacy Accessed API Reasons. Select Item 0 under Privacy API Reasons, and set its value to what you need. Select Item 0 under Privacy API Reasons, and set its value to what you need. From here on, to add new entries, all you need to do is repeat the 4 steps described above, and each time, add new values. In case you need extra Reasons/Categories for your app because of other libraries and plug-ins, those need to be in the manifest too. Time for a privacy report Time for a privacy report Luckily, Xcode can help you gather all the declared data from the privacy manifests into one single report, which comes incredibly useful in the case of a review. It comes as a PDF and looks something like the Privacy Nutrition Labels we mentioned before. To generate one, select Product, and then Archive, and select Generate Privacy Report. To Sum Up To Sum Up Now, there are numerous APIs for required reasons and collected data types that developers have to record according to Apple’s new Privacy Manifests policy, but we can get into those in another article. Overall, this initiative is great – it will allow the App Store to conduct a more thorough selection process in terms of privacy and safety, users will have a safer and more transparent experience, and developers… Well, it will take some getting used to. But it’s all for the sake of safety.

How to Get A Headstart on Apple’s Privacy Manifests

The fight for privacy is reaching its peak. Social networks like Meta, X (formerly Twitter), and TikTok are under heavy fire. The companies have to prove they are not gathering their users’ personal information, sell the data to governments around the globe, and have no intent to harm their users’ privacy and security. People all around are getting tired of worrying about the safety of their private information and taking extreme measures to protect themselves online. Online Security. So Hot Right Now. Why? One of the most fascinating processes to watch has been the United States’ fight against TikTok. The platform that is used by 170 million Americans is going to be completely banned in the United States unless its parent company, ByteDance, sells it to an American entity. In her statement, Democratic Sen. Maria Cantwell, the chair of the Senate Commerce Committee says “Congress is acting to prevent foreign adversaries from conducting espionage, surveillance, maligned operations, harming vulnerable Americans, our servicemen and women, and our U.S. government personnel”. Facebook is also in a risk zone. Cybercriminals there can take over poorly protected user accounts to spread links to fake AI applications, making the app even more unsafe, especially for vulnerable user groups. People are driven by the desire to protect themselves, their close ones, and their own data and private information. And some of us go to extreme ways to make sure it happens. Here are only a few things people do to protect themselves. Going Low Tech Not so long ago, phones had buttons. I know. Blows my mind too. What doesn’t blow it is when I see people still using them and shops still selling them. A lot of them are purchased by people who want to go off the grid, even if just for a little while. Not having any social media applications, constant push notifications, and messages with memes can, for example, save a huge amount of time. Plus, modern cool-looking, button-less phones are prone to being breached. Smishing attacks (hackers getting sensitive information via text messages), break-ins via Bluetooth, malware attacks, social engineering attacks, and so on. And people who do not want to suffer from such breaches go to extreme measures and are saying a definitive “No” to smart gadgets — phones, laptops, tablets. Rejecting the use of smart devices in the modern world is one of the toughest decisions one can take. Our lives, for better or worse, are tied to them to the extent that some people become addicted to their phones. When people start fearing for their privacy so much, they choose to toss their phones away. That is the exact time to stop and think — are we sure we are going the right way? Going low-tech is not an option for me, so I take extra care to set up two-factor authentication on my devices and make sure that sensitive data is secure or even encrypted if need be. This is true for both my personal and professional lives. A great practice for everyone who is not ready to say goodbye to their smart devices but still wants to retain their online privacy and security. Double-Checking Online Contacts In the world of global interactions, we all have online friends. People we connected with over our love for cooking, watching particular TV shows, or sharing an interest in crocheting or running. But are we sure those are all real people with good intentions? Being sure about the real personalities of people we meet online is impossible, so verifying people’s personalities is one of the best approaches to internet friendships. Here’s how to do it: Checking the consistency of communication patterns — this is one of the most popular and important tell-tales of a real person behind the profile picture. Constantly changing spelling, grammar, and communication style can actually be signs of fraud and risk. Setting up video calls — communicating with an online friend eye-to-eye with the help of video calls and building on that communication later on will not only help protect the data but will also serve as a catalyst for a relationship. Cross-platform verification — checking whether a person’s information, data, and photos on different platforms can help establish whether the connection is legitimate or fake and actually dangerous. Double-checking online contacts is a practice the SupportYourApp team implemented long ago. We make sure to not expose our information to unsuitable people and run background checks on our contacts and potential partners. Taking this extra step does take time and resources, but it also ensures that we are remaining breach- and leak-proof. Using Disposable Emails Those who have taken a “Which kind of onion are you” online test know the pain of having to enter an email address to find out whether they are a shallot. It is simply frustrating. On a more serious note, there are plenty of times when we need to enter our emails to download e-books, register on a website where we want to make a one-time purchase or keep on reading an interesting article. This makes internet users vulnerable to leaks and breaches, where their email addresses can get into the wrong hands. It is also a way to open ourselves up to one of the most annoying things on the internet — spam emails. This is where services like 10- and 15-minute-mail (not an ad) come into play. They allow users to get generic email addresses to enter whenever needed without exposing themselves to online danger. It is also a good way to spare one’s inbox from unnecessary messages — believe me, I get over 100 emails per day and these tools have been saving me from receiving thousands of spam emails over the years. A lot of people still do not recognize the importance of online security and privacy. But the further into the internet era we go, the more important these questions will become. While going off the grid and checking the legitimacy of absolutely every online connection may still be considered “going too far”, it is likely they will become standard practices for keeping our data safe. The fight for privacy is reaching its peak. Social networks like Meta, X (formerly Twitter), and TikTok are under heavy fire. The companies have to prove they are not gathering their users’ personal information, sell the data to governments around the globe, and have no intent to harm their users’ privacy and security. The fight for privacy is reaching its peak. Social networks like Meta, X (formerly Twitter), and TikTok are under heavy fire. The companies have to prove they are not gathering their users’ personal information, sell the data to governments around the globe, and have no intent to harm their users’ privacy and security. TikTok TikTok People all around are getting tired of worrying about the safety of their private information and taking extreme measures to protect themselves online. People all around are getting tired of worrying about the safety of their private information and taking extreme measures to protect themselves online. Online Security. So Hot Right Now. Why? One of the most fascinating processes to watch has been the United States’ fight against TikTok. The platform that is used by 170 million Americans is going to be completely banned in the United States unless its parent company, ByteDance, sells it to an American entity. In her statement, Democratic Sen. Maria Cantwell, the chair of the Senate Commerce Committee says “Congress is acting to prevent foreign adversaries from conducting espionage, surveillance, maligned operations, harming vulnerable Americans, our servicemen and women, and our U.S. government personnel”. Facebook is also in a risk zone. Cybercriminals there can take over poorly protected user accounts to spread links to fake AI applications, making the app even more unsafe, especially for vulnerable user groups. People are driven by the desire to protect themselves, their close ones, and their own data and private information. And some of us go to extreme ways to make sure it happens. Here are only a few things people do to protect themselves. Going Low Tech Not so long ago, phones had buttons. I know. Blows my mind too. What doesn’t blow it is when I see people still using them and shops still selling them. A lot of them are purchased by people who want to go off the grid, even if just for a little while. Not having any social media applications, constant push notifications, and messages with memes can, for example, save a huge amount of time. Plus, modern cool-looking, button-less phones are prone to being breached. Smishing attacks (hackers getting sensitive information via text messages), break-ins via Bluetooth, malware attacks, social engineering attacks, and so on. And people who do not want to suffer from such breaches go to extreme measures and are saying a definitive “No” to smart gadgets — phones, laptops, tablets. Rejecting the use of smart devices in the modern world is one of the toughest decisions one can take. Our lives, for better or worse, are tied to them to the extent that some people become addicted to their phones. When people start fearing for their privacy so much, they choose to toss their phones away. That is the exact time to stop and think — are we sure we are going the right way? Going low-tech is not an option for me, so I take extra care to set up two-factor authentication on my devices and make sure that sensitive data is secure or even encrypted if need be. This is true for both my personal and professional lives. A great practice for everyone who is not ready to say goodbye to their smart devices but still wants to retain their online privacy and security. Double-Checking Online Contacts In the world of global interactions, we all have online friends. People we connected with over our love for cooking, watching particular TV shows, or sharing an interest in crocheting or running. But are we sure those are all real people with good intentions? Being sure about the real personalities of people we meet online is impossible, so verifying people’s personalities is one of the best approaches to internet friendships. Here’s how to do it: Checking the consistency of communication patterns — this is one of the most popular and important tell-tales of a real person behind the profile picture. Constantly changing spelling, grammar, and communication style can actually be signs of fraud and risk. Checking the consistency of communication patterns — this is one of the most popular and important tell-tales of a real person behind the profile picture. Constantly changing spelling, grammar, and communication style can actually be signs of fraud and risk. Setting up video calls — communicating with an online friend eye-to-eye with the help of video calls and building on that communication later on will not only help protect the data but will also serve as a catalyst for a relationship. Setting up video calls — communicating with an online friend eye-to-eye with the help of video calls and building on that communication later on will not only help protect the data but will also serve as a catalyst for a relationship. Cross-platform verification — checking whether a person’s information, data, and photos on different platforms can help establish whether the connection is legitimate or fake and actually dangerous. Cross-platform verification — checking whether a person’s information, data, and photos on different platforms can help establish whether the connection is legitimate or fake and actually dangerous. Cross-platform verification — checking whether a person’s information, data, and photos on different platforms can help establish whether the connection is legitimate or fake and actually dangerous. Double-checking online contacts is a practice the SupportYourApp team implemented long ago. We make sure to not expose our information to unsuitable people and run background checks on our contacts and potential partners. Taking this extra step does take time and resources, but it also ensures that we are remaining breach- and leak-proof. Using Disposable Emails Those who have taken a “Which kind of onion are you” online test know the pain of having to enter an email address to find out whether they are a shallot. It is simply frustrating. On a more serious note, there are plenty of times when we need to enter our emails to download e-books, register on a website where we want to make a one-time purchase or keep on reading an interesting article. This makes internet users vulnerable to leaks and breaches, where their email addresses can get into the wrong hands. It is also a way to open ourselves up to one of the most annoying things on the internet — spam emails. This is where services like 10- and 15-minute-mail (not an ad) come into play. They allow users to get generic email addresses to enter whenever needed without exposing themselves to online danger. It is also a good way to spare one’s inbox from unnecessary messages — believe me, I get over 100 emails per day and these tools have been saving me from receiving thousands of spam emails over the years. over 100 emails per day over 100 emails per day A lot of people still do not recognize the importance of online security and privacy. But the further into the internet era we go, the more important these questions will become. While going off the grid and checking the legitimacy of absolutely every online connection may still be considered “going too far”, it is likely they will become standard practices for keeping our data safe.

Privacy And Security: How Far Are We Willing to Go to Retain Them?

This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Salim Chouaki, LIX, CNRS, Inria, Ecole Polytechnique, Institut Polytechnique de Paris; (2) Oana Goga, LIX, CNRS, Inria, Ecole Polytechnique, Institut Polytechnique de Paris; (3) Hamed Haddadi, Imperial College London, Brave Software; (4) Peter Snyder, Brave Software. Table of Links Abstract and Introduction Background Measurement Methodology Results Limitations Related Work Conclusion, Acknowledgments, and References Appendix 6 RELATED WORK Search engines and online tracking received a lot of research attention. We review studies closest to our work. A first line of work has measured to which extent we can observe personalization in search engine results [23, 34] and ads [22]. For instance, Hannak et al. [23] have developed a methodology for measuring personalization in search results, applied it to Bing, Google, and DuckDuckGo, and found that Bing results are more personalized than Google ones while they did not notice any personalization for DuckDuckGo. A second line of work has focused on solutions to protect users’ privacy from search engines and prevent web profiling. Castellà-Roca et al. [12] presented a computationally efficient protocol that provides a distorted user profile to the search engine to preserve users’ privacy. Finally, several studies have proposed privacy-preserving search-personalizing solutions for search engines. For instance, Shen et al. [36] analyze various software architectures for personalized search and envision possible strategies with a client-sided personalization. Xu et al. [40] suggest helping users choose the content and degree of detail of the profile information built by search engines. To the best of our knowledge, there is no study investigating the privacy properties of the advertising systems used on private search engines. Search engines. Several works analyzed the usage of cross-site tracking techniques in the wild [15]. Chen et al. [13] propose a data flow tracking system to measure user tracking performed through first-party cookies. They found that more than 97% of the websites they have crawled have firstparty cookies set by third-party javascript and that on 57% of them, there is at least one cookie containing a unique user identifier diffused to multiple third parties. Roesner et al. [35] measured how user tracking occurs in the wild. They found that multiple parties track most commercial pages and estimate that several trackers can each capture more than 20% of a user’s browsing behavior. Koop et al. [26] analyzed a dataset of redirection chains in the wild and found that 11% of websites redirect to the same 100 top redirectors. Moreover, they demonstrate that these top redirectors could identify users on the most visited websites. Randall et al. [33] measure the frequency of UID smuggling in the wild and find that it is performed on more than 8% of all navigations in their dataset. We use a similar method to identify user identifiers among all cookie values and query parameters by implementing automatic filtering followed by a manual inspection. All these studies were conducted in the wild, and to the best of our knowledge, no study focuses on navigational tracking techniques performed on search engines. Online tracking.

Understanding the Privacy Risks of Popular Search Engine Advertising Systems: Related Work

This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Salim Chouaki, LIX, CNRS, Inria, Ecole Polytechnique, Institut Polytechnique de Paris; (2) Oana Goga, LIX, CNRS, Inria, Ecole Polytechnique, Institut Polytechnique de Paris; (3) Hamed Haddadi, Imperial College London, Brave Software; (4) Peter Snyder, Brave Software. Table of Links Abstract and Introduction Background Measurement Methodology Results Limitations Related Work Conclusion, Acknowledgments, and References Appendix 3 MEASUREMENT METHODOLOGY We develop a measurement methodology to capture network flows when clicking on an ad from a search engine results page. Using multiple crawlers, we simulate a large number of search engine queries in order to collect a sample of information flows per search engine. For each request, we collect the cookies created, the locally stored values, and the web request sent by the browser. In addition, we rely on several open-source datasets to detect web requests to online trackers. We consider five main search engines: Google[1], Bing[2], DuckDuckGo[3], StartPage[4], and Qwant[5]. We use Google and Bing as baselines to compare with the other three, which claim to have higher privacy standards and protective measures in place. 3.1 Crawling system Each crawling iteration begins at a search engine’s main page, where our system will type a query and access the search engine results page. Next, it chooses one of the displayed ads to click on to access its destination website. Then, the navigation path passes through zero or more redirectors before landing on the ad’s destination website. The redirectors are invisible to the user but can be identified through an analysis of network requests initiated by the browser. Each of these redirectors can read the query parameters added by the search engine or other intermediaries and store them locally or send them to other third parties. The system records all first-party and third-party cookies, local storage values, and web requests at each step. We run each iteration in a new browser instance to ensure no stale data is cached from previous iterations. Depending on the search engine, ads are either part of the main page or are loaded through an iframe. We use scrapping techniques to detect them and rely on several HTML elements’ attributes. For instance, all ads on StartPage are inside an HTML element titled "Sponsored Links". Moreover, we use hyperlink values to detect Google ads since they all link to "www.googleadservices.com/*". Our system prioritizes ads with landing domains it has not visited yet, aiming to maximize the number of different destination websites. Each time a crawler clicks on an ad, our system adds the domain of its landing URL to the list of visited websites. In the subsequent iterations, the crawler first extracts the landing domains of all the displayed ads. The landing domains are included within the HTML objects of the advertisements on all search engines. The crawler then gives preference to click on ads leading to domains that have not been encountered in the list of visited websites. We reproduced these steps for 500 search queries on the five search engines. We randomly choose them from Google Trends [21] and movie titles from MovieLens [6]. All iterations were performed in "accept" cookies mode. Table 1 represents the number of different search queries we typed, the number of different destination pages we landed on, and the number of different domain paths we collected for each search engine. We implemented our system using Puppeteer [7] to automate visiting search engines’ websites, typing search queries, detecting and clicking on one of the displayed ads, and waiting for 15 seconds on the ad’s destination website. We reproduce these steps multiple times from the same IP address for each search engine. To reduce the chance of being identified as bots, we use puppeteer-extra-plugin-stealth [8]. This plugin applies various techniques to make the detection of headless Puppeteer crawlers by websites harder. Puppeteer allows us to record cookies and local storage for each request. However, it does not guarantee that it can attach request handlers to a web page before it sends any requests [33]. Hence, detecting and collecting web requests only using Puppeteer might cause losing some of them. We use a Chrome extension alongside Puppeteer crawlers to record web requests during all the crawling time. We do not observe a significant difference between web requests recorded by crawlers and web requests recorded by the extension. In median, the crawlers recorded 97% of the requests recorded by the extension. The code of the crawling system and the dataset are available at https://github.com/CHOUAKIsalim/Search_Engines_Privacy. 3.2 Detection techniques We use URL filtering to detect web requests to online trackers. We use filter rules from two open-source lists: EasyList [4] and EasyPrivacy [5]. EasyList is the most popular list to detect and remove adverts from webpages and forms the basis of many combination and supplementary filter lists [19]. EasyPrivacy is a supplementary filter list that detects and removes all forms of tracking from the internet, including tracking scripts and information collectors [19]. These filter lists are used by extensions that aim to remove unwanted content from the internet, like AdBlock and uBlock. We combined and parsed these lists using adblock-rs [1] and obtained 86 488 filtering rules. Detection of trackers: In addition, we use the Disconnect Entity List [2] to get the entities of online tracker domains. It is a dictionary where keys represent entities such as Google, Microsoft, and Facebook, and values represent the web domains that belong to each entity. Hence, to get the entity of a tracker, we iterate over all values and search to what entity is the tracker domain associated with. This list contains 1 449 entities and 3 371 related web domains. We classify an instance as bounce tracking when an advertisement’s destination link is altered to pass through one or more redirectors. To construct the redirection sequence, we trace the series of URLs the browser navigates through after clicking an ad and prior to reaching the advertisement’s intended landing page. We further validate the redirection sequence by examining the HTTP response headers, precisely the ’Location’ and ’status code’ headers. These headers divulge the redirection process, as the ’Location’ header contains the new redirection URL, and status codes such as ’301 Moved Permanently,’ ’302 Found,’ ’307 Temporary Redirect,’ and ’308 Permanent Redirect’ indicate the occurrence of redirection [17]. Detection of bounce tracking: To detect UID smuggling, we need to differentiate between query parameters that represent user identifiers and non-tracking Detection of UID smuggling and user identifiers: query parameters such as session identifiers, dates, and timestamps. We consider all query parameters, localStorage, and cookie values. We call them tokens. There are 6 971 unique tokens in our dataset. We perform the following filtering, which is similar to the one performed by Randall et al. [33]: (i) Each iteration is executed in a new browser instance; hence, user identifiers should not be shared across browser instances. We discard tokens that are the same across all or a subset of browser instances. (ii) For each browser instance and search query, we analyze the tokens resulting from the URLs of all ads that appear on the results page (which are usually in the form of googleadservices.com/..../aclk?..cid=CAESbeD2ZWCwqFv3e2k_....). We discard tokens with different values for the different ad URLs as they likely correspond to ad identifiers. (iii) To detect session identifiers, we store the profile of each iteration in a separate directory and execute an extra iteration per browser instance one day later to see which values of cookies/parameters change. We discard tokens with different values in the two iterations as they are more likely session identifiers. (iv) Similar to [33], we use programmatic heuristics to discard particular values. We discard tokens that appear to be timestamps (values between June and December 2022 in seconds and milliseconds), tokens that appear to be URLs, tokens that constitute one or more English words ([20]), and tokens that are seven characters long or less. After using these filters, we are left with 1 942 tokens. We manually investigated them and observed a non-negligible number of false positives. Hence, we manually filtered the remaining tokens and removed those composed of any combination of natural language words, coordinates, or acronyms. In the end, we are left with 1 258 user-identifying tokens, which we consider to be user identifiers. [1] https://www.google.com/ [2] https://www.bing.com/ [3] https://duckduckgo.com/ [4] https://www.startpage.com [5] https://www.qwant.com/

Understanding the Privacy Risks of Popular Search Engine Advertising Systems:Measurement Methodology

This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Salim Chouaki, LIX, CNRS, Inria, Ecole Polytechnique, Institut Polytechnique de Paris; (2) Oana Goga, LIX, CNRS, Inria, Ecole Polytechnique, Institut Polytechnique de Paris; (3) Hamed Haddadi, Imperial College London, Brave Software; (4) Peter Snyder, Brave Software. Table of Links Abstract and Introduction Background Measurement Methodology Results Limitations Related Work Conclusion, Acknowledgments, and References Appendix 2 BACKGROUND This section briefly discusses the policies of the main search engines alongside popular tracking approaches. 2.1 Private search engines We study the two dominant search engines that rely on user tracking for personalized search results and advertisements, namely Google and Bing, and three of the most popular privacy-branded search engines that provide users with nonpersonalized results and ads: DuckDuckGo, StartPage, and Qwant [11, 29]. Private search engines can either build their own independent search indexes or use big tech search engines like Bing, Google, or Yahoo to provide search results. Both types of private search engines claim not to store users’ search histories and not to collect nor share tracking and personal data. We now describe the advertising systems employed by the different private search engines and present a summary of their data-sharing policies outlined in their respective About pages. DuckDuckGo is a standalone search engine that maintains and uses its own search index alongside other indexes, such as Bing’s, to provide search results [31]. DuckDuckGo relies on Microsoft’s advertising system but only serves ads based on the search results and not the behavioral profiles of users [30]: "search ads on DuckDuckGo are based on the search results page you’re viewing instead of being based on you as a person" When clicking an ad on DuckDuckGo, the user is redirected to the ad’s landing page through Microsoft Advertising’s platform. DuckDuckGo claims Microsoft does not store ad-click behaviors from DuckDuckGo for purposes other than accounting and does not associate ad-clicks with users’ profiles [18]: "When you click on a Microsoft-provided ad that appears on DuckDuckGo, Microsoft Advertising does not associate your adclick behavior with a user profile. It also does not store or share that information other than for accounting purposes." This implies that Microsoft can, though currently chooses not to, link the ad-click to an existing Microsoft user profile. The privacy policy is signed by both DuckDuckGo and Microsoft. is a standalone EU-based search engine that allows users to access online resources without being tracked nor profiled [32]. Qwant relies on Microsoft’s advertising system to deliver ads in their search results pages. Although Qwant reports transmitting some information concerning search queries to Microsoft to enable the latter to present pertinent advertisements, it remains unclear which specific information is shared. In addition, to detect fraud, Qwant uses a specialized service offered by Microsoft, which has access to the user’s IP address and the browser "User-Agent". Qwant assures that this service does not have access to the search query, which is sent to another service that does not know the IP address of the user [32]. Qwant Unlike DuckDuckGo, which also uses Microsoft advertising, we did not find any mention to ad click information on Qwant’s privacy policy. They do not mention whether Microsoft stores this data and for what purposes they use it. is a meta-search engine that allows users to obtain non-personalized search results from Google’s search index while protecting their privacy. StartPage relies on Google AdSense to show ads to users. According to StartPage’s privacy policy, the search engine serves strictly non-personalized ads since it does not share any identifiable information with Google. Therefore, ads displayed on the search results page are solely based on the user’s search query [38]. StartPage Regarding ad-click behavior data, the privacy policy does not make any reference to whether Google tracks or profiles users based on this information. Nevertheless, StartPage emphasizes that by clicking on an ad, users leave the protection of StartPage’s privacy policies and become subject to the practices of the website they are redirected to [37]. "By clicking on an ad, like any other external website you click on after performing a StartPage search, you leave the privacy protection of StartPage and are subject to those websites’ data collection policies." 2.2 Cross-site tracking Cross-site tracking refers to the practice of following a user across multiple first-party websites and associate their browsing activities to a unique identifier. Web tracking practices require first party websites (e.g. the content providers) to share data about a user’s activity with third parties (the trackers). Online tracking has been traditionally implemented through browser cookies. However, due to increasing adoption of cookie-blocking browsers and extensions, and the push on adopting partitioned cookies storage on web browsers, more and more trackers started to rely on navigational tracking techniques. We next discuss how these techniques work. To enable cross-site cookie tracking, whenever a user visits a first-party website, the website makes a request to the third-party website (the tracker). This allows the tracker to set a cookie, which will identify the user and will be associated with the browsing activity of the user. For example, when the user visits a website A that makes a request to the tracker T, the tracker associates the cookie identifier of the user with the fact that the user visited website A (see Figure 1). Later, when the user visits website B, which also makes a request to the tracker T, the tracker will be able to associate the cookie identifier of the user with the fact that the user visited website B. Hence, the tracker will be able to know that the user visited websites A and B. 2.2.1 Cookie tracking. This was initially possible because browsers had a common cookie storage containing all cookies, and trackers could read their corresponding cookies regardless of which firstparty website allowed the tracker cookie to be set (see Figure 1). However, several browsers, such as Safari, Firefox, and Brave, have implemented partitioned storage to prevent using cookies for cross-site tracking [33]. These browsers use a partitioned cookies storage with a hierarchical namespace where a tracker accesses a different storage area on each website that loads it, preventing trackers from matching or assigning the same identifiers to users across multiple websites. Hence, cross-site tracking based on cookies can no longer be performed on these browsers. Chrome -the most used web browser- is in the process of testing partitioned cookies storage but does not use it by default [27, 28]. Navigational tracking refers to tracking techniques that use one or more URL navigations to share user information across sites. Navigational tracking does not require third-party cookies and can be used to circumvent browsers’ privacy protections from cross-site tracking using partitioned cookies storage. 2.2.2 Navigational tracking. is a navigational tracking technique that refers to redirecting users through one or more redirectors when navigating from one website to another. To allow this, a website A containing links to another website B does not directly link to the target B but instead links to an intermediary redirector (R)–the tracker (see Figure 2). When users click on a link on website A, they are taken to the redirector first, which then redirects them to the intended destination (website B) or other intermediary redirectors. The website A can directly change the actual link of the destination (b.com) to a redirection link (r.com), or a redirector’s third-party script can do it. On its turn, the redirector can change the destination link again and send it further to other redirectors. Bounce tracking Hence, from the link in the ad on the website A, one cannot know all the different redirectors the users will pass through when they click on an ad. We call the redirection path all the websites a user navigates through to arrive from A to B. Since, from a browser perspective, the redirector is the first-party domain, it can read or set cookies in its own partition [26]. In the following, we describe what data redirectors can infer according to the redirector’s behavior. (1) If the redirector does not set a first-party cookie, it will only know that a user went from website A to website B and will not be able to link this to other user browsing activities. (2) If the redirector sets a first-party cookie, it will be able to aggregate all the activity of the user that is redirected through it (either from website A or other websites that use it as a redirector), hence, it will allow cross-site tracking. (3) If the redirector also sets third-party cookies on websites A and B, it will not be able to link the activity of the user on website A with the activity of the user on website B, and with the activity of the user that goes through its own site (through redirects) since they do not share the same user ID [33]. Hence, while bounce tracking allows to a certain degree, cross-site tracking, it does not have the same coverage as the traditional third-party cookie tracking. is a navigational tracking technique that modifies users’ navigation requests by adding information to the navigation URLs in the form of query parameters. In addition, similar to bounce tracking, UID smuggling may redirect the user to one or more third-party trackers before UID smuggling redirecting the user to the intended destination. Figure 3 describes this process. When a user clicks on a link on a website A, the originator page itself or a tracker on the page–through a script–decorates the URL by adding the originator’s user identifier (UID) as a query parameter. The user then passes through zero or more redirectors which are invisible to him. Each of these redirectors can get the UID from the query parameter and has permission to store it in a first-party cookie under the redirector’s domain. Finally, the user is sent to the destination website B, and the redirector can forward or not to website B the UID it received from A. All the trackers on website B will be able to read the UID from the query parameter and know that it was the UID sent by the originator (through request headers). UID smuggling is more powerful than bounce tracking. Trackers using UID smuggling regain the ability to share UIDs across websites with different domains and can circumvent restrictions from partitioned cookie storage spaces [33]. For example, they can link the user’s visits to the website A with the user’s visits to website B and the user’s activity that goes through its site (through redirects) since they can all be linked to the same user ID. In addition, UID smuggling can help other trackers on website B (and website A) to link users’ browsing activity across all the websites that received the UID as a query parameter.

Understanding the Privacy Risks of Popular Search Engine Advertising Systems: Background

This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Salim Chouaki, LIX, CNRS, Inria, Ecole Polytechnique, Institut Polytechnique de Paris; (2) Oana Goga, LIX, CNRS, Inria, Ecole Polytechnique, Institut Polytechnique de Paris; (3) Hamed Haddadi, Imperial College London, Brave Software; (4) Peter Snyder, Brave Software. Table of Links Abstract and Introduction Background Measurement Methodology Results Limitations Related Work Conclusion, Acknowledgments, and References Appendix ABSTRACT We present the first extensive measurement of the privacy properties of the advertising systems used by privacy-focused search engines. We propose an automated methodology to study the impact of clicking on search ads on three popular private search engines which have advertising based business models: StartPage, Qwant, and DuckDuckGo, and we compare them to two dominant data-harvesting ones: Google and Bing. We investigate the possibility of third parties tracking users when clicking on ads by analyzing firstparty storage, redirection domain paths, and requests sent before, when, and after the clicks. Our results show that privacy-focused search engines fail to protect users’ privacy when clicking ads. Users’ requests are sent through redirectors on 4% of ad clicks on Bing, 86% of ad clicks on Qwant, and 100% of ad clicks on Google, DuckDuckGo, and StartPage. Even worse, advertising systems collude with advertisers across all search engines by passing unique IDs to advertisers in most ad clicks. These IDs allow redirectors to aggregate users’ activity on ads’ destination websites in addition to the activity they record when users are redirected through them. Overall, we observe that both privacy-focused and traditional search engines engage in privacy-harming behaviors allowing cross-site tracking, even in privacy-enhanced browsers. CCS CONCEPTS • Security and privacy → Privacy protections; Privacy protections; • Networks → Network measurement; Network measurement. KEYWORDS Search engines, advertising systems, cross-site tracking, privacy, measurement. ACM Reference Format: Salim Chouaki, Oana Goga, Hamed Haddadi, and Peter Snyder. 2023. Understanding the Privacy Risks of Popular Search Engine Advertising Systems. In Proceedings of the 2023 ACM Internet Measurement Conference (IMC ’23), October 24–26, 2023, Montreal, QC, Canada. ACM, New York, NY, USA, 15 pages. https://doi.org/10. 1145/3618257.3624823 1 INTRODUCTION Privacy-focused search engines such as DuckDuckGo, StartPage, and Qwant [3, 9, 10] promote a strategy of respecting users’ privacy and promise not to track users’ search and browsing behavior, all while delivering relevant search results. However, private search engines rely on advertising for revenue, and use traditional advertising platforms to deliver ads: DuckDuckGo and Qwant use Microsoft’s advertising system, while StartPage uses Google’s advertising system. These search engines are often ambiguous on the privacy properties of the ads that appear on their search page, and their consequent privacy properties remain unexplored to the best of our knowledge. In this work, we aim to fill this gap by conducting the first study of the privacy properties of the advertising systems of three major privacy-focused search engines: DuckDuckGo, StartPage, and Qwant, and how they compare to more popular search engines: Bing and Google. We investigate the privacy properties of these search engines when they: (i) present search ads to users, (ii) when a user clicks on an ad, and (iii) when the user lands on the advertiser’s page. We implement an automated measurement methodology to measure if and how users can be re-identified (hence, their privacy is compromised) when clicking on search ads on each search engine (see Section 3). We build an opensource implementation of this methodology in the form of a Puppeteer-based pipeline that simulates search queries and ad clicks. We apply this crawling methodology to the five search engines, providing a full dataset with visited websites, cookies created, locally stored values, and web requests to search engines’ servers and/or other third parties when clicking ads. We use filter rules from several major open-source lists to detect web requests to online trackers, and we propose a methodology to differentiate user identifiers from non-tracking values in query parameters and cookies values. We then present in Section 4 a systematic analysis of our dataset to investigate privacy harms before clicking an ad, during clicking an ad, and after clicking an ad and reaching the advertiser’s website. We find that users’ privacy is not harmed until users click on an ad. Privacy-focused search engines do not appear to attempt to re-identify users across visits or queries and do not include resources from, or make network requests to known trackers. However, we find that users’ privacy is compromised by studied search engines in various ways once users click on an ad. all Disappointingly, we find that all search engines record additional information about the user and/or the users’ clicks after the user has clicked on an ad. Private search engines capture data related to the clicked ad, including the ad provider, destination URL, and the ad’s position within the search results page, along with the user’s browsing data, such as the search query, device type, and browser language. Private search engines do not store user identifiers upon ad clicks, in contrast to traditional search engines that record user identifying values. Furthermore, we find that all search engines engage in navigation-based tracking. Navigation based tracking refers to tracking techniques that are redirecting users through one or more redirectors when navigating from one website to another in order to share user information across sites [33]. Navigation-based tracking does not require third-party cookies and can be used to circumvent browsers’ privacy protections from cross-site tracking using partitioned cookies storage. Alarmingly, we observe that privacy-focused search engines engage in more navigationbased tracking than non-privacy-focused ones: We observe navigational tracking on 4% ad clicks on Bing, on 100% ad clicks on Google, on 100% ad clicks on DuckDuckGo, on 86% ad clicks on Qwant, and on 100% ad clicks on StartPage. On the destination page, we check whether the search engine requires advertisers to abide by privacy-respecting practices by measuring whether advertisers include trackers or other known privacy-harming resources. We found that 93% of ads destination pages (across all five search engines) included tracker and privacy-harming resources. Finally, we check whether search engines or redirectors aid advertisers in profiling visitors by measuring the data they receive in the form of user-describing query params. We find that advertisers receive user identifiers in 68%, 92%, and 53% of cases for DuckDuckGo, StartPage, and Qwant, respectively. This practice, known as UID smuggling, enables redirectors to aggregate more user behavior data if they have scripts on the ads’ destination websites and they store the user-identifying parameters they receive. Notably, in the case of private search engines, the user-identifying parameters are not set by the search engine but by the redirectors encountered between the search engine’s and the advertiser’s sites. Our results indicate that private search engines’ privacy protections do not sufficiently cover their advertising systems. Although these search engines refrain from identifying and tracking users and their ad clicks, the presence of ads from Google or Microsoft subjects users to the privacyinvasive practices performed by these two advertising platforms. When users click on ads on private search engines, they are often identified and tracked either by Google, Microsoft, or other third parties, through bounce tracking and UID smuggling techniques. Particularly, advertisers receive unique user identifiers through query parameters in most ad clicks, which can enable cross-site tracking even in privacyenhanced browsers that block third-party cookie tracking.

Understanding the Privacy Risks of Popular Search Engine Advertising Systems: Abstract and Intro

This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Salim Chouaki, LIX, CNRS, Inria, Ecole Polytechnique, Institut Polytechnique de Paris; (2) Oana Goga, LIX, CNRS, Inria, Ecole Polytechnique, Institut Polytechnique de Paris; (3) Hamed Haddadi, Imperial College London, Brave Software; (4) Peter Snyder, Brave Software. Table of Links Abstract and Introduction Background Measurement Methodology Results Limitations Related Work Conclusion, Acknowledgments, and References Appendix 5 LIMITATIONS Our measurement methodology has some limitations. First, we only look for user identifiers transferred in query parameters and do not detect them when they are transferred in other methods. For instance, previous work [33, 39] found that trackers sometimes decorate their own URL in the document.referrer header with user identifiers and reads them on the destination page. Second, we run all our crawling iterations from the same IP address. Consequently, if some query parameters are IP address based, they will have the same value across all iterations, and thus we would not consider them as user identifiers. Finally, our results are subject to variation based on the ads we selected and the search queries we used. Different search queries could potentially trigger distinct ads and lead to diverse advertisers, potentially exhibiting different behaviors. Nonetheless, our primary objective is to demonstrate the potential for third-party tracking when interacting with ads on private search engines.

Understanding the Privacy Risks of Popular Search Engine Advertising Systems: Limitations

Understanding the Privacy Risks of Popular Search Engine Advertising Systems: Results

Stories, News & Mentions about THRIVACY

Thrivacy’s Stories on HackerNoon