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Movano Inc., a technology company, develops a smart wearable and continuous glucose monitoring device. Its device enables individuals and their healthcare partners to measure and manage their health conditions. Movano Inc. was formerly known as Maestro Sensors Inc. and changed its name to Movano Inc. in August 2018. The company was incorporated in 2018 and is based in Pleasanton, California.

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Movano Health secures strategic investment for wearable tech

/PRNewswire/ -- <strong>Movano</strong> Health (Nasdaq: MOVE), a pioneer in health technology, secured a strategic investment from a tier-one multi-billion dollar medical...

Movano Health Secures Strategic Investment from Tier-One Multi-Billion Dollar Medical Device Company

/PRNewswire/ -- <strong>Movano</strong> Health (Nasdaq: MOVE), a pioneer in health technology, today has agreed to sell approximately 45 million units in a $24 million private...

Movano Health Announces $24 Million Private Placement

Introduction Remember when Femtech was a brand-new term nobody knew? It feels like we have been using this word forever, but it has been around for less than a decade! Yet the history of products, technologies, and services addressing health issues specific or prevalent to women started long ago, ages before this umbrella term was first used in 2016 to address this rapidly evolving market. Since then, this industry has had major plot twists, from Maven Clinic becoming the first unicorn in 2021 to venture capital infusions shaking up the game; the sector is sizzling hot! Femtech And let us give a thumbs-up to the growing acceptance and issues. It is no longer a niche but a booming market, with its size estimated at over $55 billion in 2022 and . de-stigmatization of women’s health expected to surpass $100 billion by 2030 Why is it important to me? I am a digital tech specialist coming from the Artificial Intelligence/ Machine Learning field with a rich background in building and successfully deploying voice recognition, AI cameras, and virtual assistant products catering to millions. Lately, I have shifted my focus, stepping into the intriguing world of Femtech and healthcare drawn by the promise of AI to simplify and enhance healthcare solutions, changing stagnant approaches to women’s healthcare through daring initiatives. With this article providing a thoughtful inquiry into how advanced technologies like LLMs shape modern healthcare and Femtech, I invite you to join me on this exciting journey. Together, we will take a look at how boosts the Femtech market and sift through the new directions and strategies reshaping this burgeoning sector. Our aim will be not only to learn about the tendencies of the industry but also to identify key patterns that could offer thrilling opportunities. And so, if you are curious like me about how novel technologies improve healthcare experiences for women and what strategic footholds exist in the Femtech market, let us dive in and explore. AI/ML Current trends and thrilling developments in Femtech In the last few years, the Femtech sector has been undergoing a transformative shift. Gone are the days when Femtech products predominantly focused on menstrual cycle tracking and fertility planning. Today, the industry is blossoming with more specialized solutions, from advanced home-testing kits to cutting-edge wearables. Technological advances have attracted a range of strong players in a multitude of niche markets, each aiming to revolutionize different aspects of women’s health. 1. From Basic Tracking to Comprehensive Health Solutions Take Sonio, a Paris-based Femtech firm. They recently received FDA clearance for their AI product, Sonio Detect, which aims to redefine prenatal ultrasounds. What sets Sonio apart is its manufacturer-independent tool that simplifies the screening process while raising its efficiency and accuracy, offering an easy-to-use yet powerful solution. San Francisco startup Mira is pioneering another monumental shift in Femtech by embedding lab technology into a handheld fertility monitor. With a user base exceeding 76,000, Mira’s advanced fluorescent detector goes beyond traditional ovulation kits. The technology allows for daily health checks and has accumulated an impressive amount of data — over 8.5 million hormone data points and analysis of more than 670,000 menstrual cycles. With AI-driven insights into hormone fluctuations, Mira is offering more personalized and accurate predictions than ever before, quite literally putting a mini hormone lab in the palms of its users. Recently, The Inc. placed Mira at number 437 on its 2023 list of fastest-growing private companies in the US, ranking it the highest among Femtech companies. More complex products that require significant medical research are also quickly gaining momentum. Teal Health exemplifies this with its recent $8.8 million seed funding round, backed by prominent investors like Emerson Collective and Serena Ventures. The funds aim to build a comprehensive platform, starting with the crucial issue of cervical cancer. women’s telemedicine 2. The Rise of Wearable Innovations Wearable technology is another frontier where Femtech is making significant strides. One famous example is Bloomlife, a clinically validated diagnostic device for pregnant women that allows future mothers to track contractions, giving them a better awareness of their progress. Movano Health has captured attention with its Evie Ring — a wearable gadget currently under examination by the FDA. Upon receiving FDA clearance, the Evie Ring will have the potential to set new standards in women’s health monitoring, offering an integrated, round-the-clock health monitoring and tracking solution right at the user’s fingertips. Ava, a fertility and pregnancy tracking bracelet with an accuracy rate of over 90%, has already been cleared by the FDA. Established in 2014, well before we even started using the term ‘Femtech,’ Ava is a prime example of a successful Femtech startup. Having attracted over $42 million in funding, the company reported to have helped over 70 thousand women in 36 countries to get pregnant by 2020. 3. The Expansion to Different Age Groups One of the noteworthy trends in Femtech recently is the surge in startups focusing on menopause. According to Female Insights, over 30% of companies in this sector were founded in the last three years alone. This uptick is accompanied by increasing diversity in the types of solutions being offered to tackle menopausal symptoms — from mobile apps that track hormones and symptoms and platforms facilitating user interaction and community building to consumer goods and therapeutic medications. According to Pubmed, by 2030, up to 1.2 billion women will be in or approaching menopause globally, with 47 million new women joining every year. By 2025, it is estimated that over 1 billion people worldwide—about 12% of the population—will be going through menopause. Financially, the menopause product market was valued at $15 billion in 2020 and is projected to skyrocket to $23 billion by 2028. These figures illustrate a maturing market with significant financial potential, underscoring why it has become a magnet for innovation and investment. An Irish startup, IdentifyHer, offers menopausal women sensor solutions. Controlled through a smartphone app, the sensor passively gathers information on menopause symptoms without interfering with the wearer's daily activities. The app also makes use of the data gathered to track progress and assess the effectiveness of lifestyle changes. The collected data is presented in periodic reports and can be used by medical professionals to diagnose and treat menopausal symptoms. AI-driven Midday is another customized menopause product developed by US-based startup Lisa Health that offers comprehensive management of the menopause journey. Midday’s solution, including a wearable device, an AI-based symptom tracking system, and a decision support tool for hormone therapy, uses advanced tracking to determine the menopause stage and anticipate symptoms. The app is used by doctors to manage the entire menopause process and provide decision support, while users are provided with a variety of therapeutic options and informational resources to give them personalized insights and get them ready for every stage of menopause. 4. Going Beyond Just Health Although Femtech is considered, first of all, a rising star in the Medtech industry, the possibilities of women-oriented digital technology products are not limited to healthcare alone. Recently, the term started to see new applications, one of which is Women’s Safety. Indian startup Shakti Wearables manufactures Shakti, a women’s defensive wearable device that increases women's safety in dangerous situations. When the wearer activates the button on the device, a small, non-lethal current is released, electrifying an attacker, while the internal insulation of the bangle keeps the wearer safe; this gives women a chance to flee their attackers and call for assistance. Additionally, Shakti sends the wearer's most important three contacts a WhatsApp message with their most recent live location. Another interesting personal safety app is being developed by Australian startup Safely, offering women peer-to-peer support in tense or frightening circumstances. With their contacts, users of the app can create private groups or circles for internal communication and location sharing. In case of emergency, users can also switch on the high-alert mode and notify each of their designated guardians. The app then uses live monitoring, records live video and audio, sends GPS updates, and saves all of this data to the cloud for later use as evidence. Additionally, users receive safety alerts from locations of interest to help them plan their commute or travel safely. Expanding healthcare’s reach with new technologies The world of healthcare is rapidly evolving, especially when it comes to Femtech. This dynamic transformation is powered by modern innovative technologies that are reshaping how women engage with healthcare and how healthcare providers offer services tailored to them. 1. The Personalization Revolution Fueled by LLMs One of the most disruptive advancements in this domain is the rise of Large Language Models (LLMs) like ChatGPT and GPT-4. Far from being a mere tech fad, LLMs are revolutionizing healthcare, with Femtech embracing this new technology. LLMs have the potential to serve as virtual nursing assistants, supporting busy doctors in responding to patients' basic queries about medications or health conditions. As you will see from the examples below, LLMs are proving to be instrumental in facilitating the creation of highly promising Femtech products, from personalized consulting products to innovative community engagement tools. One of the most intriguing examples of the implementation of this technology in Femtech is Soula, a Cyprus-based startup that provides women with support during pregnancy, childbirth, and motherhood with its "AI-powered" assistant. Powered by Med-PaLM, an LLM developed by Google to generate answers to medical questions, the app is expected to be able to respond to inquiries about pregnancies and maternity issues and provide information on a range of health-related concerns. It will also be able to answer questions about diet, sleep, physical activity, and emotional self-care, as well as track fetal movement and kick frequency. Mexican startup Kuna utilizes an LLM-powered chatbot to give women a secure way to access information and advice on parenting and sexual health. The startup's , Kuna Chat, offers details on sex, reproduction, pregnancy, and parenthood. The startup also provides online consultations with experts for more complex or difficult questions. In this way, Kuna encourages sex education for the uneducated while offering trustworthy, practical, and concrete answers to users' queries. AI-powered chatbot 2. Cost-Effective Health Management An extraordinarily important aspect of building successful medical products is to make services affordable to the customer, reaching broader but less solvent user segments and geographies. In this vein, Femtech offers not only personalized but also cost-effective healthcare solutions. Apps often provide free basic versions, with premium services available through subscription fees. Quite obviously, the use of LLMs trained on medical data sets and specialized in specific product cases can provide a boost to the more affordable cost of the products themselves. For example, the Ava fertility bracelet mentioned in the previous section of this article costs around $249 in the U.S., and the accompanying app is free with the bracelet purchase. When considering that the average cost of infertility treatment in the U.S. is around $11,000—often not covered by basic healthcare plans—Femtech presents a more affordable starting point for many women. 3. Breaking Barriers in Remote and Rural Areas Digital technology in Femtech is also crucial for improving healthcare access in remote and rural areas, especially in low- or middle-income countries in Africa and Asia — two regions with the highest female populations. Femtech provides innovative solutions such as virtual prenatal visits and remote monitoring, to manage their health more cost-effectively, regardless of their location. empowering women One successful example of such a product is M♡THer – a virtual care product facilitating the diagnosis of gestational diabetes mellitus by measuring blood glucose levels, blood pressure, weight, and more. GDM affects up to 20% of pregnant women, leading to various birth complications, and M♡THer, developed by the Australia E-Health Research Centre, has already benefited thousands of women in remote areas of the region. 4. A Multifaceted Approach to Chronic Diseases Femtech is not solely about reproductive health; it also targets female-specific chronic diseases through a mix of prevention, societal change, and technology. Mobile apps and wearable devices make it easier to manage and monitor conditions over time, a vital component of tackling chronic diseases that affect women disproportionately. To name an example of a health issue specific to women, it is estimated that 1 in 10 women have endometriosis – the growth of endometrial tissue outside of the uterus that can cause significant problems from pelvic pain to infertility. DotLab, a US-based medical diagnostics company, pioneers medical tests in this area, applying machine learning mechanisms to diagnose and assess this currently incurable disease. On the other side of the Atlantic Ocean, Kheiron Medical Technologies, a UK Medtech startup, combines data science and radiology to help with the diagnosis of breast cancer, another terrible disease affecting predominantly women. According to published clinical trial results, the company’s breakthrough technology achieves previously unseen heights in malignancy detection in breast cancer screening. Applied Conclusions Recent years have shown rapid exponential growth in the number of Femtech companies and products, both through the use of innovative technologies and through the discovery of more and more new applications and solutions. With the Femtech market estimated to reach an imposing $100 billion globally by 2030, it is one of the areas of the modern technology economy that is expected to provide maximum lucrative opportunities for new bright ideas and prosperous businesses in the coming years. While deciding whether to follow this attractive route, aspiring Femtech entrepreneurs should consider the following market characteristics. While there are highly competitive niches in the Femtech market, specifically well-established fields like menstrual cycle and pregnancy tracking, there are also areas that are more attractive for entry. Examples of such areas include app-controlled wearables and menopause management. The largest share of the global women’s health market surprisingly pertains to chronic women’s health conditions (forecast to reach $218 billion by 2027), and only 1 percent of existing Femtech companies address this area. Did you hear that? That is an opportunity knocking. AI and specifically Large Language Models can be a powerful boost for Femtech products as (a) with proper customization, LLMs assist in scaling the product or service itself and strengthen the core part of the product, and (b) they provide magnificent tools for community development, and the community is said to be the most important part of a product! Organic communities built around products are both a way of monetization and a tool that increases retention, helping build durable community-led growth. Taking into account the fact that the Femtech market is rapidly developing with a simultaneous increase in competition, and given the general market conditions (in particular, subscription applications revenues are currently declining; even if positive dynamics have remained in the Medtech segment, it has become more difficult to attract and retain users), companies need to be cost-efficient and provide a highly personalized, secure service to survive and rise above the competitors. A relevant and extremely effective tool employed by more and more technology startups for achieving these goals, again, is AI technologies. Whether it's addressing questions about menstrual health, pregnancy, or chronic conditions, LLMs can fill the information gap quickly and efficiently. Statistically, only 6% of Femtech companies used AI in 2021, mostly in diagnostics and data analytics, and while this share has seen stable growth over the years, there is enough room for new inspired ideas and success stories in this area. All in all, the advent of heralds an exciting era for Femtech entrepreneurs. LLMs have the potential to revolutionize user engagement and content delivery, supporting busy healthcare professionals by handling preliminary patient communication, serving as virtual nursing assistants or health advisors, and simply bringing a personalized touch to technology. With cutting-edge AI technologies charging Femtech products, humanity has the chance to drastically improve the efficiency of the entire healthcare system catering to women. Finally, there is a chance to overcome the millennia-old barriers plaguing this area of medicine, such as high costs, gender bias, stigmatization of women's health-related topics, and a lack of resources. Large Language Models Powered by the latest technological advancements, Femtech waits for its next hero — and it may be you!

Beyond Wellness: How Femtech is Shaping the Future of Women's Healthcare

সুস্থতার বাইরে: কীভাবে ফেমটেক মহিলাদের স্বাস্থ্যসেবার ভবিষ্যত গঠন করছে

Beyond Wellness: Wie Femtech die Zukunft der Frauengesundheit gestaltet

Más allá del bienestar: cómo Femtech está dando forma al futuro de la atención médica de las mujeres

Au-delà du bien-être : comment Femtech façonne l'avenir des soins de santé des femmes

कल्याण से परे: कैसे फेमटेक महिलाओं के स्वास्थ्य देखभाल के भविष्य को आकार दे रहा है

ウェルネスを超えて: フェムテックが女性のヘルスケアの未来をどのように形作るか

웰니스를 넘어서: Femtech가 여성 건강 관리의 미래를 형성하는 방법

Além do bem-estar: como a Femtech está moldando o futuro da saúde feminina

За пределами здоровья: как Femtech формирует будущее женского здравоохранения

Wellness'ın Ötesinde: Femtech Kadın Sağlığı Hizmetlerinin Geleceğini Nasıl Şekillendiriyor?

Ngoài sức khỏe: Femtech đang định hình tương lai của ngành chăm sóc sức khỏe phụ nữ như thế nào

超越健康：Femtech 如何塑造女性医疗保健的未来

Meet the Winners of Startups in North and Central America

How a Herd of Models Challenges ChatGPT's Dominance: Conclusion, Discussion, and References

Authors:
(1) Johannes Rude Jensen, University of Copenhagen, eToroX Labs (johannesrudejensen@gmail.com);
(2) Victor von Wachter, University of Copenhagen (victor.vonwachter@di.ku.dk);
(3) Omri Ross, University of Copenhagen, eToroX Labs (omri@di.ku.dk). Table of Links Abstract and Introduction 1 Literature Review 2 Methodology and Artefact Requirements 3 The Implementation and Integration of the Artefact 4 Artefact Evaluation 5 Discussion 6 Conclusion and Future Work, and References 6 Conclusion and Future Work 6 Conclusion and Future Work In this paper, we present ongoing efforts towards the research question: To what extent can blockchain technology improve the execution of a leveraged trade? Utilizing the DSR methodology we demonstrate the implementation and initial integration of a digital artefact, utilizing the Dai Stablecoin system to deterministically automate the processes required to monitor and liquidate a leveraged position. The initial attempt at integrating the artefact into a traditional exchange operation presented several necessary compromises required to maintain the integrity of the secured exchange environment. Future work on this artefact will seek to bridge the chasm between permissionless blockchain technology and the hardened enterprise environment. Our work will examine the feasibility of exposing the browser-based wallet API to internally managed hot-wallets alongside an examination of whether the recursive operation can be conducted utilizing internal liquidity. References Antonopoulos, A. M., and Wood, G. 2018. Mastering Ethereum: Building Smart Contracts and Dapps, O’Reilly Media. Beinke, J. H., and Nguyen Ngoc, D. 2018. “Towards a Business Model Taxonomy of Startups in the Finance Sector Using Blockchain,” 39th International Conference on Information Systems (December), pp. 1–9. Castellanos, J. A. F., Coll-Mayor, D., and Notholt, J. A. 2017. “Cryptocurrency as Guarantees of Origin: Simulating a Green Certificate Market with the Ethereum Blockchain,” in 2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE), IEEE, pp. 367–372. Dai, J., and Vasarhelyi, M. A. 2017. “Toward Blockchain-Based Accounting and Assurance,” Journal of Information Systems (31:3), pp. 5–21. Egelund-Müller, B., Elsman, M., Henglein, F., and Ross, O. 2017. “Automated Execution of Financial Contracts on Blockchains,” Business and Information Systems Engineering (59:6), pp. 457–467. Fridgen, G., Urbach, N., and Schweizer, A. 2017. “Unchaining Social Businesses-Blockchain as the Basic Technology of a Crowdlending Platform IT-Management Im Zeitalter Der Digitalisierung View Project.” Glaser, F. 2017. “Pervasive Decentralisation of Digital Infrastructures: A Framework for Blockchain Enabled System and Use Case Analysis,” Proceedings of the 50th Hawaii International Conference on System Sciences (HICSS-50), pp. 1543–1552. Gregor, S., and Hevner, A. R. 2013. “Positioning and Presenting Design Science Research for Maximum Impact,” MIS Quarterly (37:2), pp. 337–356. Ken Peffers, Tuure Tuunanen, Marcus A. Rothenberger, and Samir Chatterjee. 2007. “A Design Science Research Methodology for Information Systems Research,” Journal of Management Information Systems (24:3), pp. 45–77. Labazova, O. 2019. “Towards a Framework for Evaluation of Blockchain Implementations,” in Fortieth International Conference on Information Systems. Lindman, J., Tuunainen, V. K., and Rossi, M. 2017. “Opportunities and Risks of Blockchain Technologies: A Research Agenda,” in Proceedings of the 50th Hawaii International Conference on System Sciences (2017), pp. 1533–1542. Moyano, J. P., and Ross, O. 2017. “KYC Optimization Using Distributed Ledger Technology,” Business & Information Systems Engineering (59:6), Springer, pp. 411–423. Müller-Bloch, C., Beck, R., and Palmund, S. 2017. “Blockchain to Rule the Waves-Nascent Design Principles for Reducing Risk and Uncertainty in Decentralized Environments,” Proceedings of the 38th International Conference on Information Systems (September), pp. 1–16. Myers, M. D., and Newman, M. 2007. “The Qualitative Interview in IS Research: Examining the Craft,” Information and Organization (17:1), pp. 2–26. Pedersen, A. B., Risius, M., and Beck, R. 2019. “Blockchain Decision Path: When to Use Blockchains? Which Blockchains Do You Mean?,” MIS Quarterly Executive (18:2), p. 24. (https://pure.itu.dk/ws/files/83594249/MISQe_BC_in_the_Maritime_Shipping_Industry_Revisio n.pdf). Rai, A. 2017. “Diversity of Design Science Research,” MIS Quarterly (41:1), iii–xviii. Ross, O., Jensen, J., and Asheim, T. 2019. “Assets under Tokenization: Can Blockchain Technology Improve Post-Trade Processing?,” in Fortieth International Conference on Information Systems, Munich 2019. Rossi, M., Mueller-Bloch, C., Thatcher, J. B., and Beck, R. 2019. “Blockchain Research in Information Systems: Current Trends and an Inclusive Future Research Agenda,” Journal of the Association for Information Systems (20:9), pp. 1388–1403. Schlagwein, D., Conboy, K., Feller, J., Leimeister, J. M., and Morgan, L. 2017. “‘openness’ with and without Information Technology: A Framework and a Brief History,” Journal of Information Technology (32:4), Palgrave Macmillan UK, pp. 297–305. Weinhardt, C., Anandasivam, A., Blau, B., Borissov, N., Meinl, T., Michalk, W., and Stößer, J. 2009. “Cloud Computing – A Classification, Business Models, and Research Directions,” Business & Information Systems Engineering (1:5), pp. 391–399. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Johannes Rude Jensen, University of Copenhagen, eToroX Labs (johannesrudejensen@gmail.com); (2) Victor von Wachter, University of Copenhagen (victor.vonwachter@di.ku.dk); (3) Omri Ross, University of Copenhagen, eToroX Labs (omri@di.ku.dk). Authors: Authors: (1) Johannes Rude Jensen, University of Copenhagen, eToroX Labs (johannesrudejensen@gmail.com); (2) Victor von Wachter, University of Copenhagen (victor.vonwachter@di.ku.dk); (3) Omri Ross, University of Copenhagen, eToroX Labs (omri@di.ku.dk). Table of Links Abstract and Introduction Abstract and Introduction 1 Literature Review 1 Literature Review 2 Methodology and Artefact Requirements 2 Methodology and Artefact Requirements 3 The Implementation and Integration of the Artefact 3 The Implementation and Integration of the Artefact 4 Artefact Evaluation 4 Artefact Evaluation 5 Discussion 5 Discussion 6 Conclusion and Future Work, and References 6 Conclusion and Future Work, and References 6 Conclusion and Future Work 6 Conclusion and Future Work In this paper, we present ongoing efforts towards the research question: To what extent can blockchain technology improve the execution of a leveraged trade? Utilizing the DSR methodology we demonstrate the implementation and initial integration of a digital artefact, utilizing the Dai Stablecoin system to deterministically automate the processes required to monitor and liquidate a leveraged position. The initial attempt at integrating the artefact into a traditional exchange operation presented several necessary compromises required to maintain the integrity of the secured exchange environment. To what extent can blockchain technology improve the execution of a leveraged trade? Future work on this artefact will seek to bridge the chasm between permissionless blockchain technology and the hardened enterprise environment. Our work will examine the feasibility of exposing the browser-based wallet API to internally managed hot-wallets alongside an examination of whether the recursive operation can be conducted utilizing internal liquidity. References Antonopoulos, A. M., and Wood, G. 2018. Mastering Ethereum: Building Smart Contracts and Dapps, O’Reilly Media. Beinke, J. H., and Nguyen Ngoc, D. 2018. “Towards a Business Model Taxonomy of Startups in the Finance Sector Using Blockchain,” 39th International Conference on Information Systems (December), pp. 1–9. Castellanos, J. A. F., Coll-Mayor, D., and Notholt, J. A. 2017. “Cryptocurrency as Guarantees of Origin: Simulating a Green Certificate Market with the Ethereum Blockchain,” in 2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE), IEEE, pp. 367–372. Dai, J., and Vasarhelyi, M. A. 2017. “Toward Blockchain-Based Accounting and Assurance,” Journal of Information Systems (31:3), pp. 5–21. Egelund-Müller, B., Elsman, M., Henglein, F., and Ross, O. 2017. “Automated Execution of Financial Contracts on Blockchains,” Business and Information Systems Engineering (59:6), pp. 457–467. Fridgen, G., Urbach, N., and Schweizer, A. 2017. “Unchaining Social Businesses-Blockchain as the Basic Technology of a Crowdlending Platform IT-Management Im Zeitalter Der Digitalisierung View Project.” Glaser, F. 2017. “Pervasive Decentralisation of Digital Infrastructures: A Framework for Blockchain Enabled System and Use Case Analysis,” Proceedings of the 50th Hawaii International Conference on System Sciences (HICSS-50), pp. 1543–1552. Gregor, S., and Hevner, A. R. 2013. “Positioning and Presenting Design Science Research for Maximum Impact,” MIS Quarterly (37:2), pp. 337–356. Ken Peffers, Tuure Tuunanen, Marcus A. Rothenberger, and Samir Chatterjee. 2007. “A Design Science Research Methodology for Information Systems Research,” Journal of Management Information Systems (24:3), pp. 45–77. Labazova, O. 2019. “Towards a Framework for Evaluation of Blockchain Implementations,” in Fortieth International Conference on Information Systems. Lindman, J., Tuunainen, V. K., and Rossi, M. 2017. “Opportunities and Risks of Blockchain Technologies: A Research Agenda,” in Proceedings of the 50th Hawaii International Conference on System Sciences (2017), pp. 1533–1542. Moyano, J. P., and Ross, O. 2017. “KYC Optimization Using Distributed Ledger Technology,” Business & Information Systems Engineering (59:6), Springer, pp. 411–423. Müller-Bloch, C., Beck, R., and Palmund, S. 2017. “Blockchain to Rule the Waves-Nascent Design Principles for Reducing Risk and Uncertainty in Decentralized Environments,” Proceedings of the 38th International Conference on Information Systems (September), pp. 1–16. Myers, M. D., and Newman, M. 2007. “The Qualitative Interview in IS Research: Examining the Craft,” Information and Organization (17:1), pp. 2–26. Pedersen, A. B., Risius, M., and Beck, R. 2019. “Blockchain Decision Path: When to Use Blockchains? Which Blockchains Do You Mean?,” MIS Quarterly Executive (18:2), p. 24. (https://pure.itu.dk/ws/files/83594249/MISQe_BC_in_the_Maritime_Shipping_Industry_Revisio n.pdf). Rai, A. 2017. “Diversity of Design Science Research,” MIS Quarterly (41:1), iii–xviii. Ross, O., Jensen, J., and Asheim, T. 2019. “Assets under Tokenization: Can Blockchain Technology Improve Post-Trade Processing?,” in Fortieth International Conference on Information Systems, Munich 2019. Rossi, M., Mueller-Bloch, C., Thatcher, J. B., and Beck, R. 2019. “Blockchain Research in Information Systems: Current Trends and an Inclusive Future Research Agenda,” Journal of the Association for Information Systems (20:9), pp. 1388–1403. Schlagwein, D., Conboy, K., Feller, J., Leimeister, J. M., and Morgan, L. 2017. “‘openness’ with and without Information Technology: A Framework and a Brief History,” Journal of Information Technology (32:4), Palgrave Macmillan UK, pp. 297–305. Weinhardt, C., Anandasivam, A., Blau, B., Borissov, N., Meinl, T., Michalk, W., and Stößer, J. 2009. “Cloud Computing – A Classification, Business Models, and Research Directions,” Business & Information Systems Engineering (1:5), pp. 391–399. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

The Future of Leveraged Trading on Blockchain

Authors:
(1) Johannes Rude Jensen, University of Copenhagen, eToroX Labs (johannesrudejensen@gmail.com);
(2) Victor von Wachter, University of Copenhagen (victor.vonwachter@di.ku.dk);
(3) Omri Ross, University of Copenhagen, eToroX Labs (omri@di.ku.dk). Table of Links Abstract and Introduction 1 Literature Review 2 Methodology and Artefact Requirements 3 The Implementation and Integration of the Artefact 4 Artefact Evaluation 5 Discussion 6 Conclusion and Future Work, and References 1 Literature Review Blockchain technology has been of evident interest to the IS community for several years, yielding multiple novel contributions of a theoretical or design driven nature (Labazova 2019). Within the financial services, scholars have explored the implementation of blockchain technology in KYC data sharing (Moyano and Ross 2017) accounting practices (Dai and Vasarhelyi 2017) post-trade processing (Ross et al. 2019) and the execution of financial contracts (Egelund-Müller et al. 2017). Beyond the purview of the financial services, scholars have pursued innovations in supply chain management (Müller-Bloch et al. 2017) and energy markets (Castellanos et al. 2017), and beyond. Through the pioneering efforts of early IS scholarship, the community is now in possession of multiple theoretically exhaustive taxonomies delineating the commercial implications of blockchain technologies in variety of settings (Beinke and Nguyen Ngoc 2018; Glaser 2017) juxtaposed by critical examinations of the practical capabilities of the technology (Pedersen et al. 2019). The growing body of theoretical literature in the field has led senior scholars to call for design driven research, exploring the practical implications of blockchain and smart contract technologies, in and outside the financial services and the enterprise setting (Lindman et al. 2017; Rossi et al. 2019). A permissionless blockchain is a type of distributed database architecture, in which a singleton state machine is maintained amongst a distributed network of nodes. State changes to the shared database require consensus amongst a majority of active nodes and cannot be changed once a transaction is submitted. Because all computations must be replicated amongst a distributed set of nodes, permissionless blockchain technology can be considered deterministic and transparent. Recent iterations of the technology have introduced a virtual machine with a higher-level programming language into the deterministic execution environment. Users can write scripts commonly referred to as “smart contracts“ (Antonopoulos and Wood 2018) facilitating the transparent and deterministic execution of a given business logic. The Dai stablecoin system is amongst the first and most successful smart contract-based applications on the Ethereum blockchain. Since its inception, the project has exhibited tremendous growth holding a $2.39bn valuation of assets locked at the time of writing, with the market capitalization of the associated governance token at a $548m market capitalization, down from an all-time-high surpassing $1bn. The most recent iteration of the Dai stablecoin system, Multi-Collateral Dai (MCD), is a smart contract system, designed to issue the collateral-backed stablecoin, ‘Dai’. The smart contract system accepts selected tokenized assets (Ross et al. 2019) and subjects the price of Dai to an algorithmic stability mechanism by which the asset exhibits a floating peg to the US dollar.[3] To withdraw Dai, a trader must collateralize a sum of tokenized assets within the smart-contract system. The collateral value guarantees the outstanding Dai, effectively ‘borrowed’ to the trader by the MCD smart contracts. The smart contract type responsible for accepting tokenized assets and issuing Dai is called a ‘vault’. To cover the credit risk exposure accepted by the MCD contracts, a ‘vault’ must always be over-collateralized by a ratio reflecting the perceived risk and volatility of the collateral asset. The MCD contracts utilize a sophisticated set of pricing oracles to continuously monitor the market price of the assets collateralized in vaults. Throughout the lifecycle of a vault, the trader can add or remove collateral assets, ensuring that the value of the collateral assets prevails above the liquidation price, at which the ‘loan’ will be liquidated, and the collateral auctioned away to arbitrageurs for Dai at prices marginally below market value. This paper is available on arxiv under CC BY 4.0 DEED license. [3] We have chosen not to elaborate on the stability mechanism stabilizing the value of Dai in this section, for resources on this topic, we recommend visiting: https://docs.makerdao.com/. Authors: (1) Johannes Rude Jensen, University of Copenhagen, eToroX Labs (johannesrudejensen@gmail.com); (2) Victor von Wachter, University of Copenhagen (victor.vonwachter@di.ku.dk); (3) Omri Ross, University of Copenhagen, eToroX Labs (omri@di.ku.dk). Authors: Authors: (1) Johannes Rude Jensen, University of Copenhagen, eToroX Labs (johannesrudejensen@gmail.com); (2) Victor von Wachter, University of Copenhagen (victor.vonwachter@di.ku.dk); (3) Omri Ross, University of Copenhagen, eToroX Labs (omri@di.ku.dk). Table of Links Abstract and Introduction Abstract and Introduction 1 Literature Review 1 Literature Review 2 Methodology and Artefact Requirements 2 Methodology and Artefact Requirements 3 The Implementation and Integration of the Artefact 3 The Implementation and Integration of the Artefact 4 Artefact Evaluation 4 Artefact Evaluation 5 Discussion 5 Discussion 6 Conclusion and Future Work, and References 6 Conclusion and Future Work, and References 1 Literature Review Blockchain technology has been of evident interest to the IS community for several years, yielding multiple novel contributions of a theoretical or design driven nature (Labazova 2019). Within the financial services, scholars have explored the implementation of blockchain technology in KYC data sharing (Moyano and Ross 2017) accounting practices (Dai and Vasarhelyi 2017) post-trade processing (Ross et al. 2019) and the execution of financial contracts (Egelund-Müller et al. 2017). Beyond the purview of the financial services, scholars have pursued innovations in supply chain management (Müller-Bloch et al. 2017) and energy markets (Castellanos et al. 2017), and beyond. Through the pioneering efforts of early IS scholarship, the community is now in possession of multiple theoretically exhaustive taxonomies delineating the commercial implications of blockchain technologies in variety of settings (Beinke and Nguyen Ngoc 2018; Glaser 2017) juxtaposed by critical examinations of the practical capabilities of the technology (Pedersen et al. 2019). The growing body of theoretical literature in the field has led senior scholars to call for design driven research, exploring the practical implications of blockchain and smart contract technologies, in and outside the financial services and the enterprise setting (Lindman et al. 2017; Rossi et al. 2019). A permissionless blockchain is a type of distributed database architecture, in which a singleton state machine is maintained amongst a distributed network of nodes. State changes to the shared database require consensus amongst a majority of active nodes and cannot be changed once a transaction is submitted. Because all computations must be replicated amongst a distributed set of nodes, permissionless blockchain technology can be considered deterministic and transparent . Recent iterations of the technology have introduced a virtual machine with a higher-level programming language into the deterministic execution environment. Users can write scripts commonly referred to as “smart contracts“ (Antonopoulos and Wood 2018) facilitating the transparent and deterministic execution of a given business logic. The Dai stablecoin system is amongst the first and most successful smart contract-based applications on the Ethereum blockchain. Since its inception, the project has exhibited tremendous growth holding a $2.39bn valuation of assets locked at the time of writing, with the market capitalization of the associated governance token at a $548m market capitalization, down from an all-time-high surpassing $1bn. deterministic transparent The most recent iteration of the Dai stablecoin system, Multi-Collateral Dai (MCD), is a smart contract system, designed to issue the collateral-backed stablecoin, ‘Dai’. The smart contract system accepts selected tokenized assets (Ross et al. 2019) and subjects the price of Dai to an algorithmic stability mechanism by which the asset exhibits a floating peg to the US dollar.[3] To withdraw Dai, a trader must collateralize a sum of tokenized assets within the smart-contract system. The collateral value guarantees the outstanding Dai, effectively ‘borrowed’ to the trader by the MCD smart contracts. The smart contract type responsible for accepting tokenized assets and issuing Dai is called a ‘vault’. To cover the credit risk exposure accepted by the MCD contracts, a ‘vault’ must always be over-collateralized by a ratio reflecting the perceived risk and volatility of the collateral asset. The MCD contracts utilize a sophisticated set of pricing oracles to continuously monitor the market price of the assets collateralized in vaults. Throughout the lifecycle of a vault, the trader can add or remove collateral assets, ensuring that the value of the collateral assets prevails above the liquidation price, at which the ‘loan’ will be liquidated, and the collateral auctioned away to arbitrageurs for Dai at prices marginally below market value. over-collateralized This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [3] We have chosen not to elaborate on the stability mechanism stabilizing the value of Dai in this section, for resources on this topic, we recommend visiting: https://docs.makerdao.com/.

Leveraged Trading on Blockchain Technology: Literature Review

X Corp. v. Center for Countering Digital Hate, INC. Court Filing, retrieved on March 25, 2024 is part of . You can jump to any part in this filing . This part is 13 of 19. HackerNoon’s Legal PDF Series here 3. Amendment The Court must next decide whether to allow X Corp. leave to amend its breach of contract claim. The Court is mindful that “[t]he anti-SLAPP statute was enacted to allow early dismissal of meritless first amendment cases aimed at chilling expression through costly, time-consuming litigation,” Metabolife Int’l, Inc. v. Wornick, 264 F.3d 832, 839 (9th Cir. 2001), but also that “granting a defendant’s anti-SLAPP motion to strike a plaintiff’s initial complaint without granting the plaintiff leave to amend would directly collide with Fed. R. Civ. P. 15(a)’s policy favoring liberal amendment,” Verizon Delaware, Inc. v. Covad Commc’ns Co., 377 F.3d 1081, 1091 (9th Cir. 2004). To be clear, the operative complaint here is not X Corp.’s “initial complaint”—X Corp. initially filed suit on July 31, 2023, see Compl., and amended about a week later, adding ECF as a defendant, see FAC. Nevertheless, the Court agrees with X Corp. that it “is not as if [the defendants] have been dragged through multiple, multiple rounds” of amendments. See Tr. of 2/29/24 Hearing at 34:8–18;[20] but see Gardner v. Martino, 563 F.3d 981, 991 (9th Cir. 2009) (“Appellants had already filed the first amended complaint as a matter of right and Verizon specifically held that a first amended complaint is subject to anti-SLAPP remedies.”) (citing Verizon Del., Inc., 377 F.3d at 1091). The question is whether X Corp. should receive leave to amend under Rule 15. See Mahoney v. Meta Platforms, Inc., No. 22-cv-02873-AMO, 2024 WL 68550, at *5–6 (N.D. Cal. Jan. 6, 2024) (holding that plaintiff failed to state a claim, but because amendment was “not clearly futile,” it was appropriate to defer consideration of defendant’s antiSLAPP motion pending filing of amended complaint). A court should “freely give leave” to amend “when justice so requires.” Fed. R. Civ. Proc. 15(a)(2). It may deny leave to amend due to “undue delay, bad faith or dilatory motive on the part of the movant, repeated failure to cure deficiencies by amendment previously allowed, undue prejudice to the opposing party by virtue of allowance of the amendment, [and] futility of amendment.” Leadsinger, 512 F.3d at 532. The Court believes that amendment would be futile, and that X Corp.’s desire to amend may well be based on a dilatory motive. At the motion hearing, X Corp. argued for the first time that there were damages that it had “not pleaded in the complaint,” but could add upon amendment, “which [are] in direct response to this scraping incident.” Tr. of 2/29/24 Hearing at 8:18–20. X Corp. described these damages as “dozens, if not over a hundred personnel hours across disciplines such as engineering and security that were spent trying to figure out not only what data was gained via access to the Brandwatch systems”—time not relevant to the breach of contract claim—“but what data was scraped, how it was done.” Id. at 8:7–11. It further referenced “anti-scraping measures implemented, again, at great expense from engineering time in tens of thousands, if not over a hundred thousand dollars to deal with this security risk that was identified via this incident.” Id. at 8:21–24; see also id. at 9:3–6 (“I’m sure we had, from what I understand, cost for the servers dealing with the load of just receiving all these requests. So expensive electricity cost at data centers, as well as loss of computing power.”). Damages stemming from X Corp.’s response to CCDH’s alleged scraping—rather than advertisers’ responses to CCDH’s reports—do not present the same constitutional concerns that the current damage allegations do. But they must still have been “within the contemplation of the parties when the contract was entered into or at least reasonably foreseeable by them at that time,” under state contract law. See Erlich, 21 Cal. 4th at 550. The Court is unpersuaded that the proposed allegations clear that hurdle. Fundamentally, neither of the two main reasons that X Corp. asserted for having to spend time and money responding to CCDH’s alleged scraping—(a) “safety and security” and (b) harm to X Corp.—make very much sense. a. Safety and Security First, X Corp. argued that it needed to expend resources responding to CCDH’s alleged breach in order to safeguard its users’ data. Over and over again, X Corp. played up its obligation to “keep [its users’] data safe,” Tr. of 2/29/24 Hearing at 7:13–14, called scraping a “security risk,” id. at 8:23–24, and declared that this case was “about” “the importance of data security,” id. at 12:10–11; see also id. at 14:9–10 (arguing that the CCDH report acknowledging scraping “raised a potential security issue”); id. at 35:13–15 (“we put up anti-scraping measures to stop this kind of thing so that users feel as if their data is safe.”); id. at 39:22–24 (“This is about the security of data and whether or not we’re going to honor ways that people try to keep it safe contractually and otherwise.”). While security and safety are noble concepts, they have nothing to do with this case. The Toxic Twitter report stated that CCDH had used the SNScrape tool, “which utilizes Twitter’s search function,” to “gather tweets from” “ten reinstated accounts,” resulting in a “dataset of 9,615 tweets posted by the accounts.” Toxic Twitter at 17. There is no allegation in the complaint, and X Corp. did not assert that it could add an allegation, that CCDH scraped anything other than public tweets that ten X platform users deliberately broadcast to the world.[21] No private user information was involved—no social security numbers, no account balances, no account numbers, no passwords, not even “gender, relationship status, ad interests etc.” See Meta Platforms, Inc. v. BrandTotal Ltd., 605 F. Supp. 3d 1218, 1273 (2022). When asked why the collecting of public tweets implicates users’ security interests, X Corp. insisted that “this all goes to control over your data,” and that users expect that they will be able to take down their tweets later, or to change them—abilities they are robbed of when the “data” is scraped. See Tr. of 2/29/24 Hearing at 40:17–24. But even assuming that it is “very important” to a user that he be able to amend or remove his proneo-Nazi tweets at some point after he has tweeted them, see id. at 40:24–25, a user can have no expectation that a tweet that he has publicly disseminated will not be seen by the public before that user has a chance to amend or remove it. While scraping is one way to collect a user’s tweets, members of the public could follow that user and view his tweets in their feeds, or use the X platform’s search tool (as SNScrape did) and view his tweets that way. X Corp.’s assertion that the scraping alleged here violates a user’s “safety and security” in his publicly disseminated tweets is therefore a non-starter. CCDH would not, and should not, have foreseen at the time of contracting that X Corp. would need to expend money to address “safety and security” issues following the scraping of ten users’ public tweets. b. Harm to X Corp. Second, X Corp. argued that it was entitled to expend resources responding to CCDH’s scraping because that scraping might have caused harm to X Corp.’s systems. X Corp. maintained that it was entitled to “dive in and understand what was done, how it was done, what data was taken, whether there was any damage,” and to conduct “a large investigation and then remedial action,” Tr. of 2/29/24 Hearing at 9:16–20, adding that “[i]t is well known in this space that if you scrape,” the scrapee will need to investigate and “put up anti-scraping measures,” id. at 35:9–14. X Corp. conceded that it is not able to prevent scraping “100 percent” of the time, but stated that it can make scraping more difficult, and should be compensated for trying to do so. Id. at 35:21–36:7.[22] The problem with this argument is that it is at odds with what X Corp. has alleged. Although social media platforms do not like it, scraping, for various ends, is commonplace. See, e.g., ACLU Br. at 7 (“Researchers and Journalists Use Scraping to Enable Speech in the Public Interest and Hold Power to Account.”); see also id. at 8–9 (collecting sources); Andrew Sellars, “Twenty Years of Web Scraping and the Computer Fraud and Abuse Act,” 24 B.U. J. Sci. & Tech. L 372, 375 (2018) (“Web scraping has proliferated beneath the shadow of the [CFAA].”); hiQ 2022 Circuit opinion, 31 F.4th at 1202 (“HiQ points out that data scraping is a common method of gathering information, used by search engines, academic researchers, and many others. According to hiQ, letting established entities that already have accumulated large user data sets decide who can scrape that data from otherwise public websites gives those entities outsized control over how such data may be put to use . . . the public interest favors hiQ’s position.”); see also id. at 1186 (“LinkedIn blocks approximately 95 million automated attempts to scrape data every day”). Some scraping activity can be harmful not only to the users of a website but also to the website itself. See, e.g., hiQ 2022 district opinion, 639 F. Supp. 3d at 954 (scraping can burden servers and inhibit a website’s performance); Ryanair DAC v. Booking Holdings Inc., 636 F. Supp. 3d 490, 503 (D. Del. 2022) (scraping can “greatly increase[] the quantifies of queries” on a website, “impair[] the . . . availability and/or usability” of a website, “and cause [a] website’s response times to deteriorate.”). Scraping can harm a website because a bot “can make many requests automatically and much more rapidly than any human could,” “request[ing] a huge amount of data from the target’s server.” See Compulife Software Inc. v. Newman, 959 F.3d 1288, 1299 (11th Cir. 2020). X Corp. has another case pending in this district, not before this Court, involving allegations of that kind of scraping. See X Corp. v. Bright Data Ltd., No. 23-cv-3698-WHA, First Amended Complaint (dkt. 36) (N.D. Cal. July 26, 2023) ¶ 1 (defendant “scrapes and sells millions of records from X Corp.’s X platform”), ¶ 98 (such actions “have diminished the server capacity that X Corp. can devote to its legitimate users, and thereby injured X Corp. by depriving it of the ability to use its personal property”), ¶ 102 (alleging “damage in the form of impaired condition, quality, and value of its servers, technology infrastructure, services, and reputation”); see also X Corp. v. John Doe 1, No. DC-23-09157, Original Petition (Tex. Dist. Ct., Dallas Co., July 6, 2023) ¶¶ 21–22, 24 (alleging “widespread unlawful scraping,” “flooding Twitter’s sign-up page with automated requests,” “severely tax[ing] X Corp.’s servers,” “impair[ing] the user experience for millions of X Corp.” users). This is not such a case. Here, CCDH is alleged to have used Twitter’s own search tool to collect 9,615 public tweets from ten Twitter users, see FAC ¶ 77; Toxic Twitter at 17, and then to have announced that it did so in a public report, see id. Assuming for present purposes that this conduct amounts to the “scraping” barred by the ToS,[23] the extent of CCDH’s scraping was not a mystery. As CCDH asked at the motion hearing, “What CCDH did and the specific tweets that it gathered, what tool it used, how it used that tool and what the results were are documented explicitly in its public report. So what is it that they’re investigating?” Tr. of 2/29/24 Hearing at 22: 3–7. Nor was this the kind of large-scale, commercial scraping—as in hiQ, as alleged in Bright Data—that could conceivably harm the X platform or overburden its servers. It is not plausible that this small-scale, non-commercial scraping would prompt X Corp. to divert “dozens, if not over a hundred personnel hours across disciplines,” see Tr. of 2/29/24 Hearing at 8:7–11, of resources toward the repair of X Corp.’s systems. Nor would such expenditures have been foreseeable to CCDH in 2019. In 2019, if CCDH had thought about the no-scraping provision in the ToS at all, it would have expected X Corp. to incur damages only in response to breaches of that provision that could actually harm the X platform. It would not have expected X Corp. to incur damages in connection with a technical breach of that provision that involved the use of Twitter’s search tool to look at ten users and 9,615 public tweets. It is clear to the Court that if X Corp. was indeed motived to spend money in response to CCDH’s scraping in 2023, it was not because of the harm such scraping posed to the X platform, but because of the harm it posed to X Corp.’s image. CCDH’s data collection allowed it to claim knowledge of what was occurring on the X platform in a cumulative sense. According to CCDH, its scraping revealed that X Corp. was generating millions of dollars in advertising revenue from previously banned accounts. See FAC ¶ 49; Kaplan Decl. Ex. B. X Corp.’s desire to keep entities like CCDH from drawing such unflattering conclusions about the X platform is entirely reasonable from a business point of view. But that motivation would not have been foreseeable to CCDH in 2019. In 2019, CCDH would not have understood, and should not have understood, that Twitter would wish to prevent scraping in order to keep cumulative information about the platform from the public view.[24] CCDH would certainly not have known that Twitter would change course on some of its content-moderation policies, reinstating what CCDH later described as “tens of thousands of accounts, including neo-Nazis, white supremacists, misogynists and spreaders of dangerous conspiracy theories,” see Toxic Twitter at 3, and then desire to shield the financial ramifications of that policy shift from the public. That is the kind of theoretical, speculative harm that California contract law rejects. See Erlich, 21 Cal. 4th at 550. Neither the “safety and security” rationale, nor the “harm to X Corp.” rationale for X Corp. spending time and money in response to CCDH’s scraping make any sense given the scraping alleged in this case. Such damages simply were not foreseeable when CCDH entered into the ToS in 2019. Accordingly, allowing X Corp. to amend in order to add the proposed new allegations about damages in connection with its response to CCDH’s scraping would be futile. The Court notes, too, that X Corp.’s motivation in bringing this case is evident. X Corp. has brought this case in order to punish CCDH for CCDH publications that criticized X Corp.—and perhaps in order to dissuade others who might wish to engage in such criticism.[25] Although X Corp. accuses CCDH of trying “to censor viewpoints that CCDH disagrees with,” FAC ¶ 20, it is X Corp. that demands “at least tens of millions of dollars” in damages—presumably enough to torpedo the operations of a small nonprofit—because of the views expressed in the nonprofit’s publications, see, e.g., id. ¶¶ 1, 3–4, 5, 12, 17–24, 38, 41, 43–52, 54–60, 62, 65–70, 77–78, 84–85, 92, 96, 98; see also PPP Br. at 1 (“X Corp. . . . seeks to silence critique rather than to counter it.”). If CCDH’s publications were defamatory, that would be one thing, but X Corp. has carefully avoided saying that they are. Given these circumstances, the Court is concerned that X Corp.’s desire to amend its breach of contract claim has a dilatory motive—forcing CCDH to spend more time and money defending itself before it can hope to get out from under this potentially ruinous litigation. See PPP Br. at 2 (“Without early dismissal, the free speech interests that the California legislature sought to protect will vanish in piles of discovery motions.”). As CCDH argued at the motion hearing, the anti-SLAPP “statute recognizes that very often the litigation itself is the punishment.” Tr. of 2/29/24 Hearing at 33:12–34:5. It would be wrong to allow X Corp. to amend again when the damages it now alleges, and the damages it would like to allege, are so problematic, and when X Corp.’s motivation is so clear. Accordingly, the Court STRIKES the breach of contract claim and will not allow X Corp. to add the proposed new allegations as to that claim. Continue Reading . Here [20] At the motion hearing, CCDH responded, not unreasonably, that “[i]t’s not exactly a[n] earth-shattering rule of law that contract damages have to be foreseeable and comport with the Constitution” and that, in “two shots at this,” X Corp. had only pled publication damages. See id. at 20:18–23. [21] While the Court recognizes that technically, who sees a post might “depend[] on the privacy settings,” see Tr. of 2/29/24 Hearing at 40:9–16, that is a red herring here. The whole reason that CCDH wrote about these ten users is that a large audience viewed their tweets. See Toxic Twitter at 3 (“The analysis shows that the ten accounts have already amassed 2.5 billion tweet impressions”). It is not a reasonable inference that these ten users had selected privacy settings that limited their posts to friends and family. [22] At times, it seemed as if X Corp. was suggesting that whenever a user breaches any term within X Corp.’s nineteen-page ToS, X Corp. is entitled to spend money to prevent further breaches of that term, and then to be compensated for that expenditure. That is not the law. If it was, then a plaintiff could state a claim merely by alleging the existence of a contract, its performance, and the defendant’s breach; there would be no separate requirement of proving foreseeable damages. But there is such a requirement. See Oasis W. Realty, 250 P.3d at 1121; Erlich, 21 Cal. 4th at 550. [23] CCDH argues that it does not. See MTD&S at 10–13. [24] CCDH framed this point thusly at the motion hearing: “the notion that CCDH . . . should have foreseen Twitter’s costs in better hiding white supremacist content on the platform is utterly implausible as a matter of law.” Tr. of 2/29/24 Hearing at 36:8–11. [25] “A recent survey of 167 academics and researchers found that over 100 studies about X Corp. have been diverted, stalled, or canceled, with over half of those interviewed citing a fear of being sued by X Corp. over their findings or data.” ACLU Br. at 14 (citing Sheila Dang, “Exclusive: Elon Musk’s X Restructuring Curtails Disinformation Research, Spurs Legal Fears,” Reuters (Nov. 6, 2023), https://www.reuters.com/technology/elon-musks-xrestructuring-curtailsdisinformation-research-spurs-legal-fears-2023-11-06). About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 25, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener

Court Decision on Amendment Request in Elon Musk's X Breach of Contract Lawsuit 

X Corp. v. Center for Countering Digital Hate, INC. Court Filing, retrieved on March 25, 2024 is part of . You can jump to any part in this filing . This part is 4 of 19. HackerNoon’s Legal PDF Series here B. Rule 12(b)(6) Under Rule 12(b)(6), the Court may dismiss a complaint for failure to state a claim upon which relief may be granted. The Court may base dismissal on either “the lack of a cognizable legal theory or the absence of sufficient facts alleged under a cognizable legal theory.” Godecke v. Kinetic Concepts, Inc., 937 F.3d 1201, 1208 (9th Cir. 2019) (cleaned up). A complaint must plead “sufficient factual matter, accepted as true, to state a claim to relief that is plausible on its face.” Ashcroft v. Iqbal, 556 U.S. 662, 678 (2009) (cleaned up). A claim is plausible “when the plaintiff pleads factual content that allows the court to draw the reasonable inference that the defendant is liable for the misconduct alleged.” Id. “Threadbare recitals of the elements of a cause of action, supported by mere conclusory statements, do not suffice” to survive a 12(b)(6) motion. Id. (citing Bell Atlantic v. Twombly, 550 U.S. 544, 555 (2007)). When evaluating a motion to dismiss, the Court “must presume all factual allegations of the complaint to be true and draw all reasonable inferences in favor of the nonmoving party.” Usher v. City of Los Angeles, 828 F.2d 556, 561 (9th Cir. 1987). “[C]ourts must consider the complaint in its entirety, as well as other sources courts ordinarily examine when ruling on Rule 12(b)(6) motions to dismiss, in particular, documents incorporated into the complaint by reference, and matters of which a court may take judicial notice.” Tellabs, Inc. v. Makor Issues & Rights, Ltd., 551 U.S. 308, 322 (2007). If a court dismisses a complaint for failure to state a claim, it should “freely give leave” to amend “when justice so requires.” Fed. R. Civ. Proc. 15(a)(2). A court may deny leave to amend due to “undue delay, bad faith or dilatory motive on the part of the movant, repeated failure to cure deficiencies by amendment previously allowed, undue prejudice to the opposing party by virtue of allowance of the amendment, [and] futility of amendment.” Leadsinger, Inc. v. BMG Music Pub., 512 F.3d 522, 532 (9th Cir. 2008). Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 25, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener

Rule 12(b)(6) and How it Impacts X's Lawsuit Against The Centre for Countering Digital Hate

TOPTAL, LLC v. DENIS GROSZ Court Filing, retrieved on February 26, 2024 is part of . You can jump to any part in this filing . This part is 2 of 7. HackerNoon’s Legal PDF Series here DISCUSSION “A party may move for summary judgment, identifying each claim or defense—or the part of each claim or defense—on which summary judgment is sought.” NRCP 56(a). “The court shall grant summary judgment if the movant shows that there is no genuine dispute as to any material fact and the movant is entitled to judgment as a matter of law.” “A factual dispute is genuine when the evidence is such that a rational trier of fact could return a verdict for the nonmoving party.” 121 Nev. 724, 731 (2005). “[T]he pleadings and other proof must be construed in a light most favorable to the nonmoving party.” The parties agree that California law governs the contracts at issue in this case. The Court finds that there are no genuine disputes as to any material fact and grants summary judgment, in part, for Toptal, on Count I of Mr. Grosz's counterclaim. Id. Wood v. Safeway, Inc., Id. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on October 11, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. media.licdn.com

Discussing Toptal, LLC v. Denis Grosz: NRCP 56(a) and Its Application

TOPTAL, LLC v. DENIS GROSZ Court Filing, retrieved on February 26, 2024 is part of . You can jump to any part in this filing . This part is 1 of 7. HackerNoon’s Legal PDF Series here This matter comes before the Court on Toptal, LLC's (“Toptal”) Motion for Summary Judgment (“Motion”). In the Motion, Toptal argues that Denis Grosz (“Mr. Grosz”) cannot prove that Toptal (a) breached the Note Purchase Agreement (the “NPA”), convertible promissory note (the “Note”), or (b) the Advisor Agreement entered into by the parties on November 14, 2012[1]; that Mr. Grosz's declaratory judgment counterclaims are superfluous; and that Mr. Grosz's counterclaims are barred by the statutes of limitations. The Court has considered the parties’ motion papers, the'arguments by the parties on September 29, 2023, and October 9, 2023, and the supplemental briefing submitted by the parties on October 4, 2023. The Court finds that there are no material disputed questions of fact with respect to part (a) of Toptal's Motion - whether Toptal breached the Note Purchase Agreement (the “NPA”) and convertible promissory note (the “NOTE”) and hereby grants that part of Toptal's Motion. Given this determination, it is not statute of limitations. California Code Section 360 cannot extend the 15 day period to elect the conversion even under Grosz's reading of the statute to extend the statute of limitations period for contracts. [2] As to Mr. Grosz's counterclaim for breach of the Advisor Agreement, Toptal argues that Mr. Grosz cannot show breach of contract because Mr. Grosz has not submitted evidence of damages. Grosz rests his damage model on the testimony of expert Gregory Nachtwey. The Court damages. Grosz rests his damage model on the testimony of expert Gregory Nachtwey. The Court will defer ruling on this counterclaim until after the hearing on the Motion in Limine concerning the admissibility of Mr. Nachtwey's opinions. FACTS AND PROCEDURAL HISTORY* In 2010, Mr. Du Val founded Toptal, a single-member LLC. Mr. Du Val is and always has been the CEO and sole member of Toptal. Mr. Grosz provided Toptal $1 million pursuant to the NPA on November 14, 2012, and, in exchange, Toptal issued him the Note. As part of that transaction, the parties also entered into an Advisor Agreement on the same day, pursuant to which Mr. Grosz agreed to advise the company in exchange for the opportunity to purchase 1.5% equity in Toptal subject to certain conditions that have not occurred to this day. Mr. Grosz asserts that Toptal breached the NPA and Note by (i) failing to convert to a C-corporation and give him equity upon his written election on November 24, 2015, and/or (ii) attempting to repay his principals, and accrued interest on the Note in March 2020. Several provisions of the NPA and Note are relevant to the Motion. First, the Note states that “[u]nless earlier converted into Conversion Shares pursuant to Section 2.2 of that certain Note Purchase Agreement dated November    , 2012 among the Company and the Lender (the ‘Purchase Agreement’), the principal and accrued interest shall be due and payable by the Company on demand by the Lender at any time after the Maturity Date.”   The Note contains a provision expressly waiving presentment and demand. The NPA defines the “Maturity Date” as “May 14, 2014.” Second, Section 2.2 of the NPA is titled, .”' Section 2.2(a) provides that “[t]he principal and unpaid accrued interest of the Note will be automatically converted into Conversion Shares upon the closing of the Next Equity Financing....” A Next Equity Financing Conversion Shares upon the closing of the Next Equity Financing....” A Next Equity Financing (a defined term in the NPA) did not take place prior to the Maturity Date and has not taken place to this day. Section 2.2(b) of the NPA states: “ At any time on or after the fifteenth (15th) day prior to the Maturity Date, if the Next Equity Financing has not occurred by that time, the Note, at Lender's election by written notice to the Company, will be converted into Common Stock of the Company such that the Lender receives shares constituting 8.333% of the Fully-Diluted Equity Securities of the Company following such conversion.” “ Rieht to Convert Note Optional Conversion. The parties dispute whether Section 2.2(b) provides Mr. Grosz with a 15-day window  before the Maturity Date in which to make an election to convert his Note to equity. For its part, Toptal argues that Mr. Grosz's ability to make an election under Section 2.2(b) ended on the Maturity Date. On the other hand, Mr. Grosz argues that Section 2.2(b) allows him to elect to convert his Note starting 15 days prior to the Maturity Date and continuing forever into the future. The parties agree that Mr. Grosz did not make an election under Section 2.2(b) prior to the Maturity Date. The parties also dispute the meaning of Section 2.2(c) of the NPA, which states: “ For clarification, the Company agrees to complete a Company Conversion prior to any conversion of the Note pursuant to this Section 2.2.” Mr. Grosz claims that this provision, read in connection with Section 2.2(b), grants him the power to force Toptal to complete a Company Conversion. In other words, Mr. Grosz claims that once he makes an election under Section 2.2(b), Toptal must convert from an LLC to a C-corporation under Section 2.2(c) so that it can issue Mr. Grosz equity. Toptal disagrees that Section 2.2(c) grants Mr. Grosz the power to force Toptal to change its corporate form. Companv Conversion Prior to Note Conversion. The NPA also contains a section titled ” which provides that “[t]he following events shall be considered Events of Default with respect to the Note following, and only upon, the declaration of default by the Note holder under Section 7.2: “ Events of Default, (a) The Company shall default in the payment of any part of the principal or unpaid accrued interest on the Note for more than thirty (30) days after the Maturity Date or at a date fixed by acceleration or otherwise;. (b) The Company shall fail to observe or perform any other obligation to be observed or performed by it under this Agreement or the Note, within 30 days after written notice from the Note holder to perform or observe the obligation.” Section 7.2 of the NPA states: “Remedies. Upon the occurrence of an Event of Default under Section 7.1 hereof, only at the option and upon the declaration of the holder of the Note, the entire unpaid principal and accrued and unpaid interest on the Note shall, without presentment, demand, protest, or notice of any kind, all of which are hereby expressly waived, be forthwith due and payable, and such holder may, immediately and without expiration of any period of grace, enforce payment of all amounts due and owing under the Note and exercise any and all other remedies granted to it at law, in equity or otherwise.” It is undisputed that Toptal did not convert to a C-corporation before the May 14, 2014, Maturity Date or pay any outstanding principal and interest to Mr. Grosz within 30 days after the Maturity Date. Continue Reading . Here [I] There are no fully executed copies of these agreements in the Record. The Court has reviewed a copy of the NPA and the Advisor Agreement containing Taso Du Val's (“Mr. Du Val”) signature on behalf of Toptal, but those agreements are missing Mr. Grosz's signature. The Court has not received an executed copy of the Note. However, the Court does not address whether the lack of signatures by all parties renders the Agreements unenforceable, because Count I of Mr. Grosz's counterclaims fails as a matter of law in any event. [2] Toptal's other arguments that Mr. Grosz's remaining counterclaims are untimely is addressed in a separate order. [3] The facts set forth in this Order are the facts “viewed in the light most favorable to the non-movant,” Mr. Grosz. 121 Nev. 724, 729 (2005) (“[W]hen reviewing a motion for summary judgment, the evidence, and any reasonable inferences drawn from it, must be viewed in a light most favorable to the nonmoving party.”); 118 Nev. 288, 293 (2002); 26 Carrington Mortg. Servs., 139 Nev. Adv. Op. 13 (2023). Wood v. Safeway, Inc., Chavez v. Sievers, see also Wishengrad v. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on October 11, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. media.licdn.com

Toptal, LLC v. Denis Grosz: Summary Judgement and Court Filing Analysis

The Watchtower Bible and Tract Society, the supervising body and publisher for the Jehovah’s Witness religious group, is using the DMCA to obtain the identity of a self-proclaimed 'apostate'. In a filing at a New York court, the group demanded that Google should hand over the identity of a person who allegedly uploaded videos of sermons to YouTube without permission. Reddit user Darkspliver, a member of the Jehovah’s Witness community, was recently targeted by the Watchtower Bible and Tract Society with claims that his postings infringed their copyrights. The case drew the attention of the EFF which mounted a defense on the user’s behalf, claiming that the subpoena against Reddit was invalid since Darkspliver’s use of Jehovah’s Witness material were covered under fair use. . The court agreed “Our client shared comments and concerns about the Watch Tower organization — something they have every right to do,” said EFF Staff Attorney Alex Moss this week. “We are glad that Darkspilver is safe from unmasking, and that a judge saw the important free speech and fair use issues at play here.” While the identity of Darkspliver is now safe, the religious organization now has a fresh target. According to documents obtained by TorrentFreak, the Watch Tower Bible and Tract Society of Pennsylvania has obtained a DMCA subpoena that requires Google to reveal the personal details of a YouTube user who uploaded Jehovah’s Witness sermons to the video platform. The subpoena targets an account called ‘JW Apostate’ which advertised itself as a place for “leaking Watch Tower videos”. At least initially, the channel had many videos listed for viewing but the DMCA subpoena application lists just five. “Watch Tower conducted a good-faith fair use analysis of numerous infringing posts on the account at issue,” the application filed at a New York court reads. “This content infringes copyrights held by Watch Tower.” Watch Tower informed the court that it had fulfilled all the requirements for obtaining a subpoena and its sole purpose is to “obtain the identity of an alleged infringer or infringers” so that copyrighted content can be protected. The allegedly-infringing YouTube channel listed a Reddit account for correspondence so on discovering the application, TorrentFreak contacted the associated user for comment. Very shortly after, the Reddit account, YouTube channel and all of its videos were deleted, leaving nothing in their wake. A day later, District Judge Cathy Seibel signed off on the Watch Tower request and ordered the clerk of the court to issue the subpoena against Google LLC. “This matter comes before the Court upon the ex parte application of movant Watch Tower Bible and Tract Society of Pennsylvania along with the Declaration of Paul D. Polidoro, Esq. and supporting documents for the signing of a Subpoena directing Google, LLC to produce the identity of entities or persons believed to be infringing on the copyright of Watch Tower Bible and Tract Society of Pennsylvania,” the order reads. “Having considered the Declaration and all documents submitted in support of the instant application, the Court finds good reason to issue an order directing the clerk to issue said subpoena.” The Watchtower Bible and Tract Society is no stranger when it comes to DMCA subpoena applications or indeed Judge Seibel. The religious group has attempted to in New York courts against Facebook and YouTube users, filing around since June 2017. obtain several subpoenas 60 applications overall Considering that the aim of most Christian-based religions is to spread the word as widely as possible around the world, it appears that those at the top of the Jehovah’s Witness group would prefer to control access to their information much more tightly. The application, proposed DMCA subpoena, and order granting subpoena can be found here ( 1 , 2 , 3 pdf) Image credit: Pixabay Originally published as “ Jehovah’s Witness Body Uses DMCA to Subpoena YouTube For ‘Apostate’ Identity ” with the Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) license

Jehovah’s Witness Body Uses DMCA to Subpoena YouTube For ‘Apostate’ Identity

By Joseph Flaherty On Friday, the Movado Group announced its , with an additional $100M in earnout potential. acquisition of LA-based startup watch manufacturer MVMT for $100M Yesterday, Phoenix-based D2C mattress startup Tuft & Needle merged with market leader Serta-Simmons. The hasn’t been disclosed, but the company reportedly had $170M in revenue and has been profitable since its inception. Forbes estimates the sale price between $200M and $800M, though based on comps, the lower end of that range seems more likely. price paid for Tuft & Needle So it’s been an enriching week for direct to consumer product startups, and here are some thoughts on what it might mean for the broader ecosystem. Exit Value is a Vanity Metric It’s quite possible for an entrepreneur to sell their company for a billion dollars and make less than a founder who sells for $100M. By limiting unnecessary dilution along the way (and if all the earnouts are met), the four founders of this pair of companies will walk away as centimillionaires. Compare that to Box, where star founder/CEO Aaron Levie’s stake was in a multi-billion dollar IPO. That’s not to take anything away from Aaron, just an illustration of how isn’t always the most important metric. worth just $45M exit value The VC playbook may have killed these deals Imagine if the founders of MVMT raised $5M at a $15 pre-money valuation at the first signs of traction. Then in 2016, after reaching $50M in sales, they raised another $20M at a $60M pre-money valuation to accelerate growth. Then picture the moment in 2018 when they got the $100M offer and are blocked from taking it by their investors. Perhaps the team would have rallied and found ways to grow into the new valuation, but a guaranteed exit worth tens of millions to the founders would have been off the table. Is there a limit to paid digital acquisition? MVMT and Tuft and Needle are both impressive startups that generated huge amounts of revenue with a pittance of private equity or even just personal savings. Tuft and Needle managed to acquire 1% of the mattress market in just six years — that’s a pitch deck cliche come to life! You could argue that these companies are among the best run startups, full stop. So why are they selling? , one of the for D2C product startups. It’s striking how many of these companies are selling at the - revenue threshold. There are obvious exceptions to this, and a few DNVBs have made it to the public markets with billions in sales, but it does suggest there is a ceiling to what social media can deliver on its own. Web Smith sharpest analysts looking at the DNVB space argues that Facebook and Instagram are slowing down as acquisition channels $150M $250M Are there just two viable financing strategies for DNVBs? MVMT was one of the stronger, more mature DNVBs, had $71M in sales in 2017, and it’s not a stretch to believe they’re on track to break $100M in sales in 2018. $100M in sales in just five years is an amazing feat for a founder, but a 1X multiple, with potential for 2X if all goes well, makes the venture math hard for many funds. As says, in order for a startup acquisition to matter to a VC firm, it needs to be at least 1X the . Unfortunately, there aren’t that many $100M VC funds that lead deals. The humble multiples common to DNVBs may create a misalignment between funds, who are mostly increasing in size, and the DNVB market, where a large portion of the good startups are selling for a $100M-$500M. Of course, there are outliers like Dollar Shave club that break the billion-dollar mark, but even a remarkable $600M win like RXBar’s acquisition by Kellogg’s would fail to move the needle for a billion-dollar fund. David Frankel fund’s size There seem to be two viable DNVB funding strategies: Build a sustainable growth engine with a seed or small A round, or; Fully embrace the “Go big or go home” playbook and try to use capital to scale to the point where you can generate a billion or more in sales to compensate for the “meh” multiples. Consumer products startups should carefully consider their funding strategy since the middle path seems fraught with danger. Note, writes lead checks from a and invests in DNVBs, so if you have a category killer, please ! Founder Collective $75M fund contact us

Watches, Mattresses, and "Meh" Multiples

ChatGPT in Classrooms: Opportunities, Challenges, and Ethical Considerations

Sound Call Graph Construction for Java Object: Conclusions, Acknowledgment, & References

This paper is available on arxiv under CC 4.0 license. Authors: (1) JOANNA C. S. SANTOS, University of Notre Dame, USA; (2) MEHDI MIRAKHORLI, University of Hawaii at Manoa, USA; (3) ALI SHOKRI, Virginia Tech, USA. Table of Links Introduction Background Seneca: Taint-Based Call Graph Construction for Object Deserialization Evaluation Results Related Work Conclusions, Acknowledgment, and References Object serialization and deserialization is widely used for storing and preserving objects in !les, memory, or database as well as for transporting them across machines, enabling remote interaction among processes and many more. This mechanism relies on re"ection, a dynamic language that introduces serious challenges for static analyses. Current state-of-the-art call graph construction algorithms does not fully support object serialization/deserialization, i.e., they are unable to uncover the callback methods that are invoked when objects are serialized and deserialized. Since call graphs are a core data structure for multiple type of analysis (e.g., vulnerability detection), an appropriate analysis cannot be performed since the call graph does not capture hidden (vulnerable) paths that occur via callback methods. In this paper, we present Seneca, an approach for handling serialization with improved soundness in the context of call graph construction. Our approach relies on taint analysis and API modeling to construct sound call graphs. We evaluated our approach with respect to soundness, precision, performance, and usefulness in detecting untrusted object deserialization vulnerabilities. Our results show that Seneca can create sound call graphs with respect to serialization features. The resulting call graphs do not incur signi!cant overhead and was shown to be useful for performing identi!cation of vulnerable paths caused by untrusted object deserialization. CCS Concepts: • - ; ; ; • - Software and its engineering Automated static analysis Software verification and validation General programming languages Social and professional topics History of programming languages. Additional Key Words and Phrases: object serialization, taint analysis, call graphs ACM Reference Format: Joanna C. S. Santos, Mehdi Mirakhorli, and Ali Shokri. 2024. Sound Call Graph Construction for Java Object Deserialization. Proc. ACM Program. Lang. 1, CONF, Article 1 (January 2024), 27 pages. 1 Introduction Static program analysis is a key component of today’s software analysis tools that bring automation into activities such as defect localization and/or !nding (e.g., [Dolby et al. 2007; Thaller et al. 2020]), vulnerability detection (e.g., [Jovanovic et al. 2006; Liu Ping et al. 2011]), information "ow analysis [Sridharan et al. 2011], code refactoring (e.g., [Khatchadourian et al. 2019]), code navigation (e.g., [Feldthaus et al. 2013]), code clone !nding (e.g., [Wyrich and Bogner 2019]), and optimization [Hines et al. 2005]. Such tools often perform multiple types of inter-procedural analysis, that leverage call graphs – data structures that indicate caller-callee relationships [Grove and Chambers 2001]. However, prior works have demonstrated that constructing a call graph for object-oriented programs is often non-trivial, expensive and/or non-feasible due to the usage of many dynamic programming language constructs. For instance, native calls, re"ection, and object serialization make it challenging to statically construct a sound call graph [Ali et al. 2019; Kummita et al. 2021; Reif et al. 2019, 2018; Smaragdakis et al. 2015; Sridharan et al. 2013]. These programming constructs are heavily used in contemporary software systems as they enable the developers to link/load new class libraries, methods, and objects and extend the programs’ functionalities [Landman et al. 2017; Reif et al. 2019]. Ignoring such constructs leads to unsound call graphs in which feasible runtime paths are missed, and call graphs cannot be used to infer the possible execution from the code [Reif et al. 2019, 2018; Sridharan et al. 2013]. To tackle this problem, previous works explored certain classes of language features, such as re"ection features [Bodden et al. 2011; Li et al. 2014, 2019; Smaragdakis et al. 2015], native (opaque) code [Smaragdakis et al. 2015], dynamic proxies [Fourtounis et al. 2018], and programs with Remote Method Invocation (RMI) [Sharp and Rountev 2006]. However, as demonstrated by Reif et al. [Reif et al. 2019, 2018], a powerful and frequently used programming construct that has been left out from the programming analysis techniques is serialization (and deserialization) of objects. Object serialization is the process of converting (the state of) an object into an abstract representation (e.g., a byte stream or JSON, etc.). The reverse process of reconstructing objects from its abstract representation is called deserialization. This is a widely used mechanism for storing and preserving objects in !les, memory, or database as well as for transporting them across machines, enabling remote interaction among processes and many more. For example, the Android API provides a Bundle object which can be used for inter-process communication between apps as well as Android’s OS with an individual app via their serialization and deserialization [Arzt et al. 2014; Enck et al. 2014]. Moreover, object (de)serialization is also used to improve the system’s performance by saving objects for later retrieval, e.g., saving a trained machine learning model to be used later without the need to retrain the algorithm. Serializing an object has other advantages, such as being readable by applications in other languages. For instance, JavaScript running in a web browser can natively serialize and deserialize objects to and from JSON, therefore interact with other applications written non-JavaScript languages. Although object serialization is widely used in many languages and commonly adopted by programmers, static analyzers do not fully cover analysis of programs with this construct yet [Reif et al. 2019, 2018]. This is particular important considering the spike of vulnerabilities related to untrusted object deserialization [Muñoz and Schneider 2018; Sayar et al. 2023; Schneider and Muñoz 2016] that cannot be automatically detected because call graphs are unsound. For example, Apache’s Log4j software library (versions 2.0-beta9 to 2.14.1) had an untrusted object deserialization vulnerability that allowed remote code execution. This was a critical vulnerability that affected several software systems. As demonstrated by previous studies on the soundness of call graph construction approaches [Reif et al. 2019, 2018]— guaranteeing that all possible behaviors are modeled in a call graph — state-of-theart techniques do not support serialization-related operations. They fall shortly in having nodes and edges that represent callback methods that are invoked during the serialization or deserialization of objects. There are multiple reasons on why it is hard to handle this language construct: — Serialization and deserialization uses several overridable callback method(s). These call back methods are invoked by the Java API using “non-trivial” re"ective calls that current techniques [Landman et al. 2017] for taming re"ection do not address. Therefore, the resulting call graph underapproximate the program’s behavior; they miss potential program paths through these call back methods. — The invoked callbacks during deserialization methods depend on the received object, which is coming from an external stream. The values and internal !eld types are only known at runtime when the object deserialization occurs. — The external stream may include objects whose types are not observed statically, i.e., they are available in the classpath (imported libraries, or Java built-in API) but were never actually used (instantiated) in the application scope. A typical static analysis would consider these types as unused. Therefore, existing techniques on addressing re"ections has failed to address call-graph generation with the presence of object serialization/de-serialization [Reif et al. 2019]. As such, potential program "ows are disregarded in existing call graph construction algorithms. Since the call graph is a core data structure in performing many inter-procedural code analyses, the underlying client would suffer with the unsoundness. In use-cases such as detection of untrusted deserialization vulnerabilities, an appropriate analysis cannot be performed since the call graph does not capture hidden (vulnerable) paths that occur via callback methods. There are two algorithms that (partially) handle serialization constructs (i.e., CHA [Dean et al. 1995] and RTA [Bacon and Sweeney 1996]) but they are imprecise; they abstract program executions to consider more paths than those feasible in the program. Therefore, they introduce spurious nodes and edges, rendering large call graphs. Relying on such algorithms for downstream analyses (e.g., vulnerability detection) makes the analysis imprecise, resulting in a high amount of false positives. A recent line of work [Santos et al. 2021, 2020], presented an approach (named Salsa) for providing support for serialization-related features. Although Salsa aids the static analyses of programs that uses Java’s serialization/deserialization API, it is not enough to find hidden (potentially) malicious paths in the program. Salsa relies on API modeling for abstracting the serialization/deserialization protocol which dictates callback methods control and data "ow. Specially, it relies on downcasts in the program to infer the callbacks invoked during deserialization. However, malicious objects often violate downcasts and are crafted in such way that it triggers the exploit during deserialization, i.e., the exploit executes before the downcast is performed [Dietrich et al. 2017a]. Therefore, we introduce in this paper Seneca, a novel approach that handles the challenge of constructing call graphs for programs that uses serialization features. Specially, we are focusing on improving the call graph’s soundness for Java programs with respect to serialization and deserialization callbacks without greatly affecting its precision. Seneca performs a novel taint-based call graph construction, which relies on the taint state of variables when computing possible dispatches for callback methods. The contributions of this work are: — a novel taint-based call graph construction algorithm to improve call graphs’ soundness with respect to deserialization callbacks. It is agnostic to the underlying pointer analysis method used to construct a call graph, and it is meant to complement them. — an evaluation of the approach’s soundness, precision, and scalability. Our experiments demonstrated that our approach soundly handled all the six different callbacks that can be invoked during serialization or deserialization. — a publicly available implementation of Seneca [1]. The rest of this paper is organized as follows: Section 2 describes the serialization and deserialization mechanism and the challenges in creating a call graph that is sound with respect to this feature. Section 3 explains our approach. Subsequently, Section 4 presents the evaluation of the approach whereas Section 5 presents the results. Section 6 contextualizes our approach within the state-of-the art. Section 7 concludes the paper and make final considerations. [1] The scripts to reproduce the paper results are currently available on an anonymous repository SenecaRepo. The source code for Seneca will be released upon publication and submitted for artifact evaluation

Sound Call Graph Construction for Java Object Deserialization: Introduction

This paper is under CC 4.0 license. available on arxiv Authors: (1) Bartosz Kusmierz, IOTA Foundation 10405 Berlin, Germany & Department of Theoretical Physics, Wroclaw University of Science and Technology, Poland bartosz.kusmierz@pwr.edu.pl; (2) Roman Overko, IOTA Foundation 10405 Berlin, Germany roman.overko@iota.org. Table of Links. Abstract and Introduction Related Work and Methodology Results Summary and References IV. SUMMARY In this article, we analyzed the wealth distribution of the richest addresses in various cryptocurrencies. This included the time evolution of statistical metrics like the approximated Zipf’s law coefficient, Shannon entropy, Gini coefficient, and Nakamoto coefficient, along with their average values. It was shown that coins and ERC20 tokens have quantitatively different distributions of wealth. In particular, the values of approximated Zipf’s law coefficient for coins are 0.4—1.25 and 0.7—2.25 for ERC20 tokens. Differences between the two groups were also apparent during the study of wealth centralization. It was observed that tokens are, in general, much more centralized than coins with higher Gini coefficients and smaller Nakamoto coefficients. This research might be of particular interest to DAOs and DPoS-based blockchains which rely on some form of a committee of the richest token holders. Presented values of statistical metrics like approximated Zipf coefficient or Nakamoto coefficient might help to model a committee selection process and make it more secure. Future work will evolve in two directions. Firstly, we are working on incorporating more metrics, analyzing more tokens, and considering more different sample size values. Secondly, the main findings of this work will be used to model a committee selection process. REFERENCES [1] M. Castro and B. Liskov. Practical Byzantine Fault Tolerance. In Proceedings of the Third Symposium on Operating Systems Design and Implementation, OSDI ’99, page 173–186, USA, 1999. USENIX Association. [2] W. Chen, T. Zhang, Z. Chen, Z. Zheng, and Y. Lu. Traveling the Token World: A Graph Analysis of Ethereum ERC20 Token Ecosystem. In Proceedings of The Web Conference 2020, WWW ’20, page 1411–1421, New York, NY, USA, 2020. Association for Computing Machinery. [3] X. Gabaix. Zipf’s Law for Cities: An Explanation. The Quarterly Journal of Economics, 114(3):739–767, 08 1999. [4] J. R. Jensen, V. von Wachter, and O. Ross. How Decentralized is the Governance of Blockchain-based Finance: Empirical Evidence from four Governance Token Distributions, 2021. [5] C. I. Jones. Pareto and Piketty: The Macroeconomics of Top Income and Wealth Inequality. Journal of Economic Perspectives, 29(1):29–46, February 2015. [6] D. Kondor, M. Posfai, I. Csabai, and G. Vattay. Do the Rich Get Richer? ´ An Empirical Analysis of the Bitcoin Transaction Network. PLOS ONE, 9(2):1–10, 02 2014. [7] S. Kusmierz, Bartosz nd M ´ uller and A. Capossele. Committee Selection ¨ in DAG Distributed Ledgers and Applications. In K. Arai, editor, Intelligent Computing, pages 840–857, Cham, 2021. Springer International Publishing. [8] D. Larimer. EOS.IO White Paper, 2017. https://github.com/EOSIO/ Documentation/blob/master/TechnicalWhitePaper.md. [9] D. Lehnberg. Valuing the top 50 Ethereum ERC-20 tokens using Metcalfe’s Law, 2018. [10] C. Li and B. Palanisamy. Comparison of Decentralization in DPoS and PoW blockchains. In International Conference on Blockchain, pages 18–32. Springer, 2020. [11] Q. Lin, C. Li, X. Zhao, and X. Chen. Measuring decentralization in bitcoin and ethereum using multiple metrics and granularities, 2021. [12] A. Miller, Y. Xia, K. Croman, E. Shi, and D. Song. The Honey Badger of BFT Protocols. page 31–42, 2016. [13] S. Nakamoto. Bitcoin: A Peer-to-Peer Electronic Cash System. 2008. [14] K. Pietrzak. Simple Verifiable Delay Functions. 2018. https://eprint. iacr.org/2018/627. [15] S. Somin, G. Gordon, and Y. Altshuler. Network Analysis of ERC20 Tokens Trading on Ethereum Blockchain. In A. J. Morales, C. Gershenson, D. Braha, A. A. Minai, and Y. Bar-Yam, editors, Unifying Themes in Complex Systems IX, pages 439–450, Cham, 2018. Springer International Publishing. [16] S. Somin, G. Gordon, A. Pentland, E. Shmueli, and Y. Altshuler. ERC20 Transactions over Ethereum Blockchain: Network Analysis and Predictions, 2020. [17] C. Stathakopoulou, T. David, and M. Vukolic. Mir-BFT: HighThroughput BFT for Blockchains. 06 2019. [18] E. Syta, P. Jovanovic, E. K. Kogias, N. Gailly, L. Gasser, I. Khoffi, M. J. Fischer, and B. Ford. Scalable Bias-Resistant Distributed Randomness. In 2017 IEEE Symposium on Security and Privacy (SP), pages 444–460, 2017. [19] F. Victor and B. K. Luders. Measuring Ethereum-Based ERC20 Token ¨ Networks. In I. Goldberg and T. Moore, editors, Financial Cryptography and Data Security, pages 113–129, Cham, 2019. Springer International Publishing. [20] C. Wang, X. Chu, and Y. Qin. Measurement and Analysis of the Bitcoin Networks: A View from Mining Pools. In 2020 6th International Conference on Big Data Computing and Communications (BIGCOM), pages 180–188, 2020. [21] B. Wesolowski. Efficient verifiable delay functions. 2018. https://eprint. iacr.org/2018/623. [22] K. Wu, S. Wheatley, and D. Sornette. Classification of Cryptocurrency Coins and Tokens by the Dynamics of Their Market Capitalizations. Royal Society Open Science, 5(9):180381, 2018. [23] G. K. Zipf. Selected Studies of the Principle of Relative Frequency in Language. Harvard University Press, 2013.

How Centralized is Decentralized? Summary and References

Innovating Identity Verification: An Introduction to Proof of Personhood (PoP) Protocols

All you need to know about the security loopholes in your android device and how to protect your mobile phones from privacy invasions and security breaches. From “Don’t forget your keys ” to “ Don’t forget your phone”, technology has evolved at a lightning speed that the key to our life is now lying in open pockets. An average person spends more than 5 hours a day on their phone and most people worldwide start their day with their cellphone rather than a toothbrush. The first thing we check in a house is if the door and its locks are properly working as we would do anything to secure our personal space but do we have the same concern when we use our mobile phones. It’s high time that we understand our mobile phone is a superkey that can open any door to our life, be it virtual or physical. What can you do to secure your mobile phones? The tremendous rise in mobile device usage has increased the number of malware rapidly. The time you surf the internet or the time you are using a service over the internet; You may think of yourself as the customer, but you are the ones getting sold in reality because nothing ever comes free in life. Be wary of App permissions Recent studies have often found that over 1000 android apps are stealing your data like location, passwords, and other personal ones. NEVER GIVE UNNECESSARY APP PERMISSIONS. For example, consider the situation where you have downloaded a shopping app. If it’s asking for permissions like storage, contacts, and microphone; Think about this why does a shopping app need access to your storage, with that permission, it can get access to anything that’s on your memory, and if it is asking for a microphone it can listen to your audio conversations or calls. A legitimate app asks permission only based on its functions. To manage app permissions Go to > > select an app> settings apps permissions Mandatory password rules: Always use a long password of a minimum of 8 characters. It should be a mix of uppercase, lowercase characters, numbers, symbols. Keep a habit of changing your password every 60 days. Never repeat the same password for multiple accounts. Some sites may make these rules mandatory and some ignore it. Have you ever thought about why we need to stick to the above 4 rules? This is the number one rule to protect your accounts against password attacks. BruteForce What is a BruteForce attack? In a BruteForce attack, the attacker tries different combinations of characters and symbols until it matches with your password. Now that we have computers with greater computational power therefore a BruteForce attack can be done easily with any of the plain passwords you use. is a website that lets you check if our email-id or password appears to be stolen anywhere on the internet. haveibeenpwned.com -This website will inform you how long it might take a hacker to crack your chosen password howsecureismypassword.net : LESS IS MORE Uninstall if there are any unused apps. Because in reality think of the apps as the windows to your house. The lesser they are the more secure you are. : Avoid apps from third-party sites Download your apps only from the Google Play store or the Apple play store. Always set up Multifactor authentication: The first factor of authentication is (passwords and pin). If an attacker got access to your password and tries to access your account, they will ask for a second factor that is (token, OTP, authenticator). This token is set with time constraints that it changes every minute so that even if the attacker got hold of your password then they have to have access to your phone. And not only the access to the phone but they have to have the PIN from the authenticator app or the token you received. This is why we say Something you know something you have Hackers hate two-factor authentication Ensure that you use a safe and secure browser instead of chrome because browser security matters The browser knows so much about us than any other person in our life. It knows the things you search for, the passwords you put in, your medical history, the people you chat with, what you are, and where you are. when it comes to browser security choose a browser that focuses on both privacy and security. check the privacy of your browser with . Always browse websites with http HTTP panopticlick s, not and are known for offering adequate levels of privacy and security. The difference is that in firefox we have to manually add plugins like Noscripts and ghostery for ensuring privacy and security but in brave, they have a built-in tracker blocker, ad blocker, and browser fingerprinting blocker. Mozilla Firefox Brave Always use a when you are browsing from a VPN public wifi An open public wifi hotspot is like the hermitage of all hackers and snoopers as the open network does not have any security standard practices. With the use of a basic sniffing tool, an attacker can steal all the data like passwords you used for login especially social websites, bank account, email, etc. A VPN can send your network traffic through a secure encrypted tunnel making it difficult for others who are trying you intercept your data. VPN comes with several cool features like giving access to region-restricted content, keeps your browsing private from ISP’S and encrypts the network traffic over the internet. It offers both privacy and security. . Be skeptical about links Never trust any links blindly. Attackers can use tactics to steal your data. Always hover your mouse over any link that you receive in email or social media website to see if it’s taking you to the right page. social engineering This website can be used to analyze suspicious files and URLs.Add to chrome. It can reveal the exact URL of any shortened links and also lets you know if the site is classified as a malicious one or not. virustotal : unshorten.link It can reveal the exact URL of any shortened links and also lets you know if the site is classified as a malicious one or not. Add unshorten.link to chrome. Download like Malwarebytes for your mobile device. anti-malware If you do happen to download a malicious app or open a malicious attachment, mobile anti-malware protection can prevent the infection. ( settings>security>google play protect) which is google’s built-in malware protection for android. Click here to learn Enable Google Play Protect more if u find any sort of performance issues like frequent crashing of apps, annoying popup ads on your screen, and unusual drop in speed and performance as these can be the signs of malware infections. Boot your device to safe mode (switch off, then press power button + volume down ) Malware is malicious software that can steal sensitive data, take control of your device, and damage your system. If your phone is infected by a recently installed app, the safe mode allows you to test to see if the app that you just installed has caused an issue on your phone. It’s never too late to take control of your data. It’s your data, your life, and your responsibility. Lead Photo by on Biljana Jovanovic pixabay

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