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Our definition of 'goal setting' isn't what it used to be. Somewhere along the line, we got it twisted – and now, kids who have barely got their driver's license are expected to plan their entire future. We carry this pressure into adulthood, too. I know plenty of people who have nailed down exactly where they're (allegedly) going to be in five years' time. Despite the supposed benefits of future planning, they're very stressed individuals. Here's what I think happens. Job titles, cities, and companies are all tangible things we can grasp with our minds. It's easy to say, "I plan to be a Senior Vice President of something or other at this company in five years" – so we hold on tight to those specifics. The problem is that, in prescribing a future for yourself, you leave little room for unexpected opportunities or a change of heart. You box yourself in when there was never a need to use boxes in the first place. Let me dig into this a little deeper – you'll see what I mean. Why We Prescribe I am forever haunted by the statistic that end up dropping out. The actual dropping out part doesn't scare me; it's the implication. 40 percent of US college students We feel so pressured to line up our ducks, to prescribe our futures, that the detours and roadblocks we encounter later on are seen as failures. Even as adults, why are we so hellbent on having a rigid five-year or ten-year plan? How on earth can we predict what we'll want, who we'll be, and where we'll be in a decade? If I made decisions now based on what I wanted ten years ago... well, things would be different. Let's put it that way. So, why do we do this? We're Conditioned This Way It's easy to see how rigid planning stems from childhood; we're raised with the ideas of career progression, degrees, and qualifications, and having defined career paths that lead logically from one place to the next. There's little talk of flexibility. At my school, you were either going to college, or you were lost. It stands to reason that we'd come away from this conditioning with a set-in-stone mindset: you choose your career now, and you plan it out to the nth degree. We Want A Sense of Security of Dr. Thema Bryant-Davis, a psych professor at Pepperdine University, "It’s a way of dealing with worry and can give people a false sense of control." In the words I can understand that. I've been there myself. When all else feels chaotic, there's a temptation to retreat into our comfort zone and make rigid plans for the future. We don't want any surprises – we just want what we expect. Of course, we know deep down that life has other ideas. Planning our lives seems like the best way to combat unexpected changes... but in reality, it's like locking our knees before hitting the ground. We make ourselves so inflexible that even a short fall leaves us with a broken bone. Family and Societal Expectations It's easy to start prescribing your life as a way to explain or justify yourself to others. If you're not in a stable job at the moment, for instance, maybe you'll create a plan for the future to soothe family members, friends, and peers. But again, this comes with an expiration date. We may be able to fool others for a little while – but we can't keep up that façade forever. It's human nature to change our minds and switch paths; trying to stick it out despite feeling like you're going in the wrong direction is counterintuitive at best, and damaging at worst. But Isn't Planning Helpful, Usually? Great point – yes, it is. Planning is both helpful and essential to getting the life you want. But there is a difference between planning and prescribing yourself to a certain path. Planning involves setting goals, making budgets, and taking stock of what you already have. It's like mapping out the route to your destination, but knowing that you can change course if needed. I'd also say that mindful planning comes from your values and passions. Instead of looking at a career progression chart for your area of study, you should plan your future around the things you know bring you the most joy. Why? Because typically, your values and passions are the things that don't change. The Psychology of Open-Ended Planning I frequently think about this quote by one of the best philosophers of all time, Ludwig Wittgenstein: “A man will be imprisoned in a room with a door that’s unlocked and opens inwards; as long as it does not occur to him to pull rather than push." It's hard to know exactly what he's referring to here. The way I interpret it, though, is that doors to new opportunities will remain closed if you don't realize they're able to be opened. This is what I believe happens to us when we plan our lives out too rigidly. We end up clueless as to the possibilities lying just outside our peripheral – because why would we bother looking when everything is already decided? Open-ended planning sidesteps this problem by actively putting questions in the air. What could you be doing in five years' time? What could your career path look like? What new opportunities can you take advantage of in the meantime to explore your passions? You acknowledge the infinite options available to you, and therefore, it does occur to you to "pull rather than push." Values-Based Planning 101 Rounding up everything I've said so far, here's my hypothesis: we need to be engaging in values-based planning rather than prescriptive planning. There's a clear distinction between the two. With prescriptive planning, you are largely focused on the way your future looks and sounds. What's your job title? What do you earn? In values-based planning, you're focusing on how your future feels. Knowing what you respond to emotionally, and knowing where your passions and values lie, you're able to construct a picture of the future that will gel with your personality regardless of where you end up. A rigid, prescriptive, plan might be, "I want to finish this degree, get a job in X field and work my way up the ladder within Y company." A flexible, open-ended five-year plan might be, "I want to end up in a position where I'm working with people on an individual level and helping them to solve problems." That could mean a lot of different things – so you can take the time to explore and find out what that looks like in practice. You can see how one plan is much more open to unexpected opportunities and changes, while the other has a much narrower path. Yes, the more ambiguous option feels a little intimidating – but that's the beauty of it. A little uncertainty is a small price to pay for flexibility and uncapped potential. When Prescriptive Planning Helps By all means, there are certain situations where making a rigid timeline can be your saving grace. For instance, you might be working toward a 10-mile run in six months' time. You might have a specific career path in mind that requires certain qualifications, or you may be working toward a promotion at your current job – in which case you'll need to plan out the steps to get there and make sure they're achievable within a certain timeframe. In these cases, planning a rigid timeline is your roadmap – it keeps you on the path and provides a sense of security in the face of uncertainty. As soon as you start talking about five or ten years down the track, though, you're essentially making plans for someone you don't even know. That person could have completely different wants and needs than you do now. By all means, keep your goals in sight – but make sure you're open to the unexpected and embrace opportunities as they come. That's how real growth happens. Wrap Up It's easy to get caught up in what feels like a logical plan; life rarely works out the way we expect it to. (In fact, it usually deviates in very drastic ways). That doesn't mean you should abandon all hope for progress – just that you should always be ready to adapt when necessary. Center yourself around the things that ignite your passions, and don't put a low ceiling over your head as a self-inflicted cap on your potential. I'd love to hear from you if anything here resonated, so feel free to respond by email or add a comment below. Until next time! If you enjoyed this article, I’d love to hear from you. Reply to this or at me and I'll do my best to get back to everyone! email tweet @ScottDClary

It's Important to Plan for the Future, But Don’t Prescribe It

Since the wave of digitization is sweeping businesses, managing an enterprise has become a much easier task. Enterprises now utilize applications or software like Customer Relationship Management or CRM, project management applications, ERP, and more to carry out several business-critical tasks. However, the widespread use of these applications may also come at a cost as data breaches have been becoming more common since 2020. For this reason, Identity and access management or IAM architects have shed light on the importance of enterprise application security. Vulnerabilities in an Enterprise Application Enterprises are always on the lookout for opportunities to drive digital transformation. They do so by introducing certain technological innovations. For example, the creation of enterprise IoT and Cloud computing, and more. Although these innovations have significantly contributed to the digitization of an enterprise and its subsequent growth, they can also be an entry point for hackers and malware. These entry points are known as vulnerabilities in an application. Let’s take a look at a few of the most common ones found in applications. Lack of Access Control A surprising number of data breaches take place internally. That is, the employees working within the company may consciously or unconsciously break access protocol. It can also occur when user restrictions are not implemented properly. Therefore, a lack of control over who accesses what in the application is considered to be a vulnerability. Exposure of Sensitive Data This can be a big problem for the enterprises that collect extensive information about their employees and their customers. This involves information like addresses, , account details, etc. The databases that house this information are among the most common targets for cyberattacks and may result in the unauthorized distribution of sensitive data. passwords Non-functioning Authentication Authentication is a measure that is present in every enterprise application. This security measure is essential as it plays a vital role in the aforementioned user restriction. Applications usually utilize the security along with the session management application functions to ensure security. security two-factor authentication Security Misconfigurations Finally, applications could also suffer from security misconfigurations. These can be due to various reasons like default configurations that are not secure, misconfigured HTTP headers, or incomplete configurations. Tools that Promote Enterprise Application Security At present, the tools that an enterprise uses to ensure the security of their applications can be categorized into two types. Firstly, there are the tools that test the existing security measures put in place in the application. The other type of tool is the one that fortifies or adds to the security measures. 1. Types of testing tools The testing is carried out using the following tools: Static application testing: This tool essentially reads the code that the application is created from to ensure no mistakes that could lead to security gaps. Dynamic application testing: These tools detect gaps in the code while it is running. To a developer, this kind of testing mimics a cyberattack so that the developers can work towards reinforcing the code. Hence, the name dynamic. 2.  Variations of Fortifying Tools Under the fortifying tools, one will find the following: RASP: The Runtime Application Self-protection tool acts as a tester and a fortifier simultaneously. It does so by monitoring the environments present on the desktop and even mobile devices. Anti-Tampering software: This software functions to prevent cybercriminals from obtaining access to the code of an application. Threat detector: This tool is usually used by professionals to ensure that the environment in which the application is running is safe. Therefore, aspects like the state of the environment, potential threats, and compromised devices can be found. Benefits of Implementing Enterprise Application Security From the aforementioned points, it becomes clear that an enterprise application with the required security measures offers an unprecedented sense of safety to the users of the applications. In addition to this, other benefits make application security desirable to IAM architects and enterprises alike. These benefits include: Real-time detection: Cyberattacks are usually detected quite quickly, given the number of security measures that are put into place. Some measures also detect high-risk activity through behavior analysis. The detection can also be automated in some cases. Versatile integration: The security measures are compatible with multiple applications, irrespective of whether it is a cloud platform or a SaaS platform. Compliance monitoring: The testing tools mentioned before will be used to ensure that the application complies with the safe practices and regulations of data security. These tools can also monitor the critical infrastructure and prevent possible data loss. Enterprise application security (EAS) is transforming the information technology landscape. As the pace of digital business accelerates and the complexity of enterprise IT grows, regulatory requirements are demanding more serious attention. No longer a “nice-to-have”, many boardrooms now acknowledge that is essential to securing infrastructure, data, and applications in today’s digital economy. enterprise application security

Why IAM Architects Prescribe Enterprise Application Security

Is Artificial Intelligence in Healthcare a Ticking Time Bomb or Timely Solution?

Virtually all industries can benefit from artificial intelligence (AI), but few see improvements as meaningful as healthcare. While medical AI faces considerable obstacles, it’s hard to ignore its potential. Applications in chronic disease management are particularly noteworthy. Roughly 129 million people in the U.S. have a chronic health condition. Despite many of these diseases being treatable, they’re also behind five of the 10 most common causes of death. Consequently, managing them is becoming increasingly crucial, and AI can help in several ways. Improved Diagnoses and Prognoses The use of AI to treat chronic conditions starts with a patient’s diagnosis. Because machine learning excels at detecting subtle trends in data, it can often identify diseases early and with impressive accuracy. AI even outperformed doctors at detecting skin cancer in 61.2% of studies and matched human experts in another 29% of cases. Similarly, predictive analytics models can predict how an individual patient’s case may progress. This insight into the future helps patients and doctors understand the situation so they can better decide how to treat the condition. These predictions become increasingly reliable as AI gathers additional data. Personalized Healthcare Once treatment begins, AI can improve health outcomes by tailoring care plans to individual patients. The same disease may play out entirely differently between patients with varying biologies, lifestyles and socioeconomic backgrounds. Consequently, healthcare is most effective when it’s specific to the individual, and AI provides the granular data analysis necessary to personalize it. AI-powered personalized care apps already exist. Some generate meal plans for individual users to help them meet unique nutritional goals based on their background and biology. Others predict how different medicines may perform for varying patients. Across all cases, machine learning can take the trial and error out of finding the best treatment, leading to more effective long-term care. Automated Reminders and Updates As patients try to stick to their nutrition and medicine plans, AI apps can offer automated reminders to help. Many people don’t take their meds as prescribed, leading to 125,000 deaths annually in the U.S. AI can monitor data from wearables to detect when patients may be veering off their treatment plans and alert them so they can respond before it’s too late. Similarly, AI monitoring tools can provide doctors with real-time updates on how their patients’ conditions are progressing. In some cases, machine learning algorithms could then use this data to predict if a change of course would yield better results. Even without that level of automation, AI’s timely insights enable faster, effective care changes. Streamlined Administrative Tasks There are behind-the-scenes benefits to AI in chronic disease management, too. Smart algorithms can automate administrative work like scheduling, data entry and health documentation compliance. As a result, doctors and nurses get more time to spend with patients. Improving efficiency and accuracy in these tasks also produces financial advantages. Administrative work accounts for up to 25% of healthcare spending in the U.S., so removing errors and minimizing the time spent on it leads to considerable savings. As more hospitals take advantage of this opportunity, the industry could justify lower costs to become increasingly accessible. Potential Downsides to AI in Chronic Disease Management While the upsides to AI in chronic disease management show significant promise, there are some complications to consider, too. Before the technology can reach its full potential, medical organizations must grapple with issues of bias, hallucinations and data security. Bias One of the biggest challenges of AI in healthcare is its tendency to replicate and even exaggerate human prejudices. Machine learning’s accuracy varies between demographics — facial recognition misidentifies Asian and Black faces up to 100 times more often than white faces. Similar issues could make AI diagnoses far less reliable for patients of color. Human bias throughout history means there’s less data on Black patients and other minorities. Consequently, AI may be unable to accurately diagnose their conditions or determine which treatments will be most effective. Failing to recognize this gap could worsen the inequality in care already plaguing the U.S. medical system. Hallucinations There’s also the hallucination issue to deal with. Even the most accurate machine learning models can hallucinate, thanks to various data-related problems and the fact that AI cannot identify facts — it only sees trends in data. In many applications, hallucinations cause minor hiccups, but they could be dangerous in a chronic disease context. Doctors could over-rely on AI and fail to account for its tendency to hallucinate. Once that happens, they could prescribe treatments based on false or misleading predictions, possibly leading to risky health complications. Human experts must always have the final say, but the mere presence of AI suggestions can lead to complacency and overreliance. Data Privacy and Security AI models also require a significant amount of data, and medical information is highly sensitive. This combination raises questions about patient privacy and cybersecurity. What if an AI service accidentally discloses personal information about patients? What if AI datasets make hospitals a bigger target for cybercriminals? Healthcare has already seen a troubling rise in cybercrime. The industry suffered 809 data compromises in 2023, more than double the year before. Security standards will likely need to evolve before hospitals can safely feed large volumes of patient information to AI models. AI Could Revolutionize Chronic Disease Care While obstacles remain, AI has big potential in the realm of managing chronic diseases. As the industry develops regulations and best practices around using this technology safely, its promise will only grow. Increasing use and technological development will lead to better patient outcomes, lower costs and streamlined medical workflows, benefiting everyone involved. Virtually all industries can benefit from artificial intelligence (AI), but few see improvements as meaningful as healthcare. While medical AI faces considerable obstacles, it’s hard to ignore its potential. Applications in chronic disease management are particularly noteworthy. Virtually all industries can benefit from artificial intelligence (AI), but few see improvements as meaningful as healthcare. While medical AI faces considerable obstacles, it’s hard to ignore its potential. Applications in chronic disease management are particularly noteworthy. Roughly 129 million people in the U.S . have a chronic health condition. Despite many of these diseases being treatable, they’re also behind five of the 10 most common causes of death. Consequently, managing them is becoming increasingly crucial, and AI can help in several ways. 129 million people in the U.S 129 million people in the U.S Improved Diagnoses and Prognoses The use of AI to treat chronic conditions starts with a patient’s diagnosis. Because machine learning excels at detecting subtle trends in data, it can often identify diseases early and with impressive accuracy. AI even outperformed doctors at detecting skin cancer in 61.2% of studies and matched human experts in another 29% of cases. in 61.2% of studies in 61.2% of studies Similarly, predictive analytics models can predict how an individual patient’s case may progress. This insight into the future helps patients and doctors understand the situation so they can better decide how to treat the condition. These predictions become increasingly reliable as AI gathers additional data. Personalized Healthcare Once treatment begins, AI can improve health outcomes by tailoring care plans to individual patients. The same disease may play out entirely differently between patients with varying biologies, lifestyles and socioeconomic backgrounds. Consequently, healthcare is most effective when it’s specific to the individual, and AI provides the granular data analysis necessary to personalize it. AI-powered personalized care apps already exist. Some generate meal plans for individual users to help them meet unique nutritional goals based on their background and biology. Others predict how different medicines may perform for varying patients. Across all cases, machine learning can take the trial and error out of finding the best treatment, leading to more effective long-term care. generate meal plans for individual users generate meal plans for individual users Automated Reminders and Updates As patients try to stick to their nutrition and medicine plans, AI apps can offer automated reminders to help. Many people don’t take their meds as prescribed, leading to 125,000 deaths annually in the U.S. AI can monitor data from wearables to detect when patients may be veering off their treatment plans and alert them so they can respond before it’s too late. leading to 125,000 deaths annually leading to 125,000 deaths annually Similarly, AI monitoring tools can provide doctors with real-time updates on how their patients’ conditions are progressing. In some cases, machine learning algorithms could then use this data to predict if a change of course would yield better results. Even without that level of automation, AI’s timely insights enable faster, effective care changes. Streamlined Administrative Tasks There are behind-the-scenes benefits to AI in chronic disease management, too. Smart algorithms can automate administrative work like scheduling, data entry and health documentation compliance. As a result, doctors and nurses get more time to spend with patients. Improving efficiency and accuracy in these tasks also produces financial advantages. Administrative work accounts for up to 25% of healthcare spending in the U.S., so removing errors and minimizing the time spent on it leads to considerable savings. As more hospitals take advantage of this opportunity, the industry could justify lower costs to become increasingly accessible. 25% of healthcare spending 25% of healthcare spending Potential Downsides to AI in Chronic Disease Management While the upsides to AI in chronic disease management show significant promise, there are some complications to consider, too. Before the technology can reach its full potential, medical organizations must grapple with issues of bias, hallucinations and data security. Bias One of the biggest challenges of AI in healthcare is its tendency to replicate and even exaggerate human prejudices. Machine learning’s accuracy varies between demographics — facial recognition misidentifies Asian and Black faces up to 100 times more often than white faces. Similar issues could make AI diagnoses far less reliable for patients of color. up to 100 times more up to 100 times more Human bias throughout history means there’s less data on Black patients and other minorities. Consequently, AI may be unable to accurately diagnose their conditions or determine which treatments will be most effective. Failing to recognize this gap could worsen the inequality in care already plaguing the U.S. medical system. Hallucinations There’s also the hallucination issue to deal with. Even the most accurate machine learning models can hallucinate, thanks to various data-related problems and the fact that AI cannot identify facts — it only sees trends in data. In many applications, hallucinations cause minor hiccups, but they could be dangerous in a chronic disease context. Doctors could over-rely on AI and fail to account for its tendency to hallucinate. Once that happens, they could prescribe treatments based on false or misleading predictions, possibly leading to risky health complications. Human experts must always have the final say, but the mere presence of AI suggestions can lead to complacency and overreliance. Data Privacy and Security AI models also require a significant amount of data, and medical information is highly sensitive. This combination raises questions about patient privacy and cybersecurity. What if an AI service accidentally discloses personal information about patients? What if AI datasets make hospitals a bigger target for cybercriminals? Healthcare has already seen a troubling rise in cybercrime. The industry suffered 809 data compromises in 2023 , more than double the year before. Security standards will likely need to evolve before hospitals can safely feed large volumes of patient information to AI models. 809 data compromises in 2023 809 data compromises in 2023 AI Could Revolutionize Chronic Disease Care While obstacles remain, AI has big potential in the realm of managing chronic diseases. As the industry develops regulations and best practices around using this technology safely, its promise will only grow. Increasing use and technological development will lead to better patient outcomes, lower costs and streamlined medical workflows, benefiting everyone involved.

How AI is Transforming Chronic Disease Management

The Reddit Paradox: How AI is Driving Users Back to the Internet’s Most Un-AI Misfit 

የ Reddit ፓራዶክስ፡ AI እንዴት ተጠቃሚዎችን ወደ በይነመረብ በጣም የአይ-አይ ሚስጢት ይመለስላቸዋል። 

Reddit Paradoksu: Süni intellekt istifadəçiləri İnternetin ən qeyri-AI uyğunsuzluğuna necə qaytarır 

Парадокс Reddit: як штучны інтэлект вяртае карыстальнікаў да самых непрыдатных у Інтэрнэце людзей 

Το παράδοξο του Reddit: Πώς η τεχνητή νοημοσύνη οδηγεί τους χρήστες πίσω στο Διαδίκτυο που δεν ταιριάζει με την τεχνητή νοημοσύνη 

La paradoja de Reddit: cómo la IA está haciendo que los usuarios vuelvan a Internet sin la IA adecuada

रेडिट विरोधाभास: कैसे AI उपयोगकर्ताओं को इंटरनेट के सबसे अन-AI मिसफिट की ओर वापस ले जा रहा है

Paradoks Reddita: Kako umjetna inteligencija tjera korisnike natrag u najneprilagođeniju umjetnu inteligenciju na internetu 

Reddit のパラドックス: AI がいかにしてユーザーをインターネットの最も AI に適さない場所へと引き戻しているのか

Reddit 역설: AI가 사용자를 인터넷의 가장 AI에 맞지 않는 곳으로 되돌리는 방식

O paradoxo do Reddit: como a IA está levando os usuários de volta à Internet O desajustado mais anti-IA

Kitendawili cha Reddit: Jinsi AI Inawarudisha Watumiaji kwenye Misfit ya Mtandaoni ya Un-AI 

Парадокс Reddit: як штучний інтелект повертає користувачів до найбільш непридатного для ШІ Інтернету 

Transforming Legacy with Domain-Driven Design, III: Tactic

Enhancing RAG with Knowledge Graphs: Integrating Llama 3.1, NVIDIA NIM, and LangChain for Dynamic AI

নলেজ গ্রাফের সাথে RAG উন্নত করা: ডাইনামিক AI-এর জন্য Llama 3.1, NVIDIA NIM, এবং LangChain ইন্টিগ্রেট করা

Vylepšení RAG pomocí Knowledge Graphs: Integrace Llama 3.1, NVIDIA NIM a LangChain pro dynamickou umělou inteligenci

Ενίσχυση RAG με Γραφήματα Γνώσης: Ενσωμάτωση Llama 3.1, NVIDIA NIM και LangChain για Dynamic AI

Mejora de RAG con gráficos de conocimiento: integración de Llama 3.1, NVIDIA NIM y LangChain para IA dinámica

تقویت RAG با نمودارهای دانش: ادغام Llama 3.1، NVIDIA NIM و LangChain برای هوش مصنوعی پویا

Amélioration de RAG avec des graphiques de connaissances : intégration de Llama 3.1, NVIDIA NIM et LangChain pour l'IA dynamique

A RAG fejlesztése tudásgráfokkal: A Llama 3.1, az NVIDIA NIM és a LangChain integrálása a dinamikus AI-hoz

ナレッジグラフによる RAG の強化: 動的 AI のための Llama 3.1、NVIDIA NIM、LangChain の統合

Kotombola RAG na ba graphiques ya boyebi: Kosangisa Llama 3.1, NVIDIA NIM, mpe LangChain mpo na AI Dynamique

RAG uzlabošana ar zināšanu diagrammām: Llama 3.1, NVIDIA NIM un LangChain integrēšana dinamiskajam AI

Aprimorando o RAG com gráficos de conhecimento: integrando Llama 3.1, NVIDIA NIM e LangChain para IA dinâmica

Kobcinta RAG garaafyada aqoonta: Isku-dhafka Llama 3.1, NVIDIA NIM, iyo LangChain ee Dynamic AI

Authors:
(1) Haleh Hayati, Department of Mechanical Engineering, Dynamics and Control Group, Eindhoven University of Technology, The Netherlands;
(2) Nathan van de Wouw, Department of Mechanical Engineering, Dynamics and Control Group, Eindhoven University of Technology, The Netherlands;
(3) Carlos Murguia, Department of Mechanical Engineering, Dynamics and Control Group, Eindhoven University of Technology, The Netherlands, and with the School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, Australia. Table of Links Abstract and Introduction Problem Formulation Affine Solution to Problem 1 Privacy Guarantee General Guidelines for Implementation Case Studies Conclusion and References III. AFFINE SOLUTION TO PROBLEM 1 In this section, we construct a prescriptive solution to Problem 1 using random affine maps. Let the user data encoding map π1(·), the immersion map π2(·), and the utility map π3(·) be affine functions of the form: We can now state the proposed solution to Problem 1 Proof: Proposition 1 follows from the discussion provided in the solution section above, Section II Remark 2 In Proposition 1, we present the first part of the design of the proposed immersion-based coding mechanism for privacy. Note that the proposed scheme is independent of the nature of the user data yk and utility uk, and does not require any assumption on the original algorithm. These features make the proposed scheme suitable to enforce privacy for a large class of linear and nonlinear high-dimensional algorithms. Remark 3 This manuscript primarily focuses on immersionbased coding for privacy of discrete-time algorithms operating in the cloud. It is noteworthy that it can be proved that the application of the proposed coding and target algorithm in Proposition 1 can be extended for privacy of continuous-time systems. This extension highlights the wider application of the immersion-based coding, providing a solution for cloud-based privacy covering discrete and continuous-time algorithms. A. Algorithms with Different Time-Scale The class of algorithms considered in (1), and the results that followed, operates on a single time scale. That is, the algorithm reacts to the received yk, iterates once according to f(·) and g(·), and sends the utility uk back to the user. This is repeated sequentially between the user and the cloud. Algorithms that fit this class are, e.g., control, monitoring, and federated learning schemes. Note, however, that there are many algorithms that operate on a different time scale from that of the user. That is, there are algorithms that receive the user data yk at time k, iterate locally multiple times in the cloud (say for t = 1, . . . , T), and only after a number of local iterations (T), the data utility uk is sent back to the user. Algorithms that fit this class are, for instance, general learning algorithms where it is often the case that the complete data set is uploaded to the cloud, the training is done there by running gradient-like steps multiple times, and only when the cost function has decreased to an acceptable level, the final trained model is sent back to the use For the sake of completeness, we concisely provide the corresponding coding result for algorithms with different time scales. Consider discrete-time dynamic algorithms of the for Further, consider the corresponding higher-dimensional target algorithm: Following the reasoning described in the above section, for algorithm (19) and corresponding target algorithm (20), we let the user data encoding map, the immersion map, and the utility map be affine functions of the form: Problem 2 is an analogue to Problem 1 for systems with two time scales. Following the lines of the solution to Problem 1, we provide a solution to Problem 2 in the following corollary of Proposition 1. Proof: The proof of Corollary 1 follows the same lines as the proof of Proposition 1. Only the time scales of the encoded signals change. The proof is omitted here for the sake of brevity. So far, we have presented the proposed immersion-based coding for two classes of discrete-time algorithms built around stochastic affine maps. Note that the only constraint on these maps is that they are full-rank. In the next section, we state sufficient conditions for the proposed privacy scheme to guarantee a prescribed level of differential privacy. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Haleh Hayati, Department of Mechanical Engineering, Dynamics and Control Group, Eindhoven University of Technology, The Netherlands; (2) Nathan van de Wouw, Department of Mechanical Engineering, Dynamics and Control Group, Eindhoven University of Technology, The Netherlands; (3) Carlos Murguia, Department of Mechanical Engineering, Dynamics and Control Group, Eindhoven University of Technology, The Netherlands, and with the School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, Australia. Authors: Authors: (1) Haleh Hayati, Department of Mechanical Engineering, Dynamics and Control Group, Eindhoven University of Technology, The Netherlands; (2) Nathan van de Wouw, Department of Mechanical Engineering, Dynamics and Control Group, Eindhoven University of Technology, The Netherlands; (3) Carlos Murguia, Department of Mechanical Engineering, Dynamics and Control Group, Eindhoven University of Technology, The Netherlands, and with the School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, Australia. Table of Links Abstract and Introduction Abstract and Introduction Problem Formulation Problem Formulation Affine Solution to Problem 1 Affine Solution to Problem 1 Privacy Guarantee Privacy Guarantee General Guidelines for Implementation General Guidelines for Implementation Case Studies Case Studies Conclusion and References Conclusion and References III. AFFINE SOLUTION TO PROBLEM 1 In this section, we construct a prescriptive solution to Problem 1 using random affine maps. Let the user data encoding map π1(·), the immersion map π2(·), and the utility map π3(·) be affine functions of the form: We can now state the proposed solution to Problem 1 Proof : Proposition 1 follows from the discussion provided in the solution section above, Section II Proof Proof Remark 2 In Proposition 1, we present the first part of the design of the proposed immersion-based coding mechanism for privacy. Note that the proposed scheme is independent of the nature of the user data yk and utility uk, and does not require any assumption on the original algorithm. These features make the proposed scheme suitable to enforce privacy for a large class of linear and nonlinear high-dimensional algorithms. Remark 2 In Proposition 1, we present the first part of the design of the proposed immersion-based coding mechanism for privacy. Note that the proposed scheme is independent of the nature of the user data yk and utility uk, and does not require any assumption on the original algorithm. These features make the proposed scheme suitable to enforce privacy for a large class of linear and nonlinear high-dimensional algorithms. Remark 3 This manuscript primarily focuses on immersionbased coding for privacy of discrete-time algorithms operating in the cloud. It is noteworthy that it can be proved that the application of the proposed coding and target algorithm in Proposition 1 can be extended for privacy of continuous-time systems. This extension highlights the wider application of the immersion-based coding, providing a solution for cloud-based privacy covering discrete and continuous-time algorithms. Remark 3 This manuscript primarily focuses on immersionbased coding for privacy of discrete-time algorithms operating in the cloud. It is noteworthy that it can be proved that the application of the proposed coding and target algorithm in Proposition 1 can be extended for privacy of continuous-time systems. This extension highlights the wider application of the immersion-based coding, providing a solution for cloud-based privacy covering discrete and continuous-time algorithms. A. Algorithms with Different Time-Scale A. Algorithms with Different Time-Scale The class of algorithms considered in (1), and the results that followed, operates on a single time scale. That is, the algorithm reacts to the received yk, iterates once according to f(·) and g(·), and sends the utility uk back to the user. This is repeated sequentially between the user and the cloud. Algorithms that fit this class are, e.g., control, monitoring, and federated learning schemes. Note, however, that there are many algorithms that operate on a different time scale from that of the user. That is, there are algorithms that receive the user data yk at time k, iterate locally multiple times in the cloud (say for t = 1, . . . , T), and only after a number of local iterations (T), the data utility uk is sent back to the user. Algorithms that fit this class are, for instance, general learning algorithms where it is often the case that the complete data set is uploaded to the cloud, the training is done there by running gradient-like steps multiple times, and only when the cost function has decreased to an acceptable level, the final trained model is sent back to the use For the sake of completeness, we concisely provide the corresponding coding result for algorithms with different time scales. Consider discrete-time dynamic algorithms of the for Further, consider the corresponding higher-dimensional target algorithm: Following the reasoning described in the above section, for algorithm (19) and corresponding target algorithm (20), we let the user data encoding map, the immersion map, and the utility map be affine functions of the form: Problem 2 is an analogue to Problem 1 for systems with two time scales. Following the lines of the solution to Problem 1, we provide a solution to Problem 2 in the following corollary of Proposition 1. Proof : The proof of Corollary 1 follows the same lines as the proof of Proposition 1. Only the time scales of the encoded signals change. The proof is omitted here for the sake of brevity. Proof Proof So far, we have presented the proposed immersion-based coding for two classes of discrete-time algorithms built around stochastic affine maps. Note that the only constraint on these maps is that they are full-rank. In the next section, we state sufficient conditions for the proposed privacy scheme to guarantee a prescribed level of differential privacy. 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

Privacy in Cloud Computing through Immersion-based Coding: Affine Solution to Problem 1

Privacy in Cloud Computing through Immersion-based Coding: Abstract and Introduction

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