Technological advancement has transformed our lives by bringing unparalleled convenience and efficiency. Data, particularly consumer data, essential for influencing businesses and developing personalized experiences, is at the heart of this transition. Companies may improve consumer satisfaction and loyalty by using data analysis to customize their products and services. However, the collection and utilization of consumer data raise privacy concerns. Protecting customers’ personal information is essential to maintaining trust, respecting individual autonomy, and preventing unauthorized access or misuse. Along with the protection of data, transparency is also another essential factor. When companies or organizations deal with users’ data, they are liable to inform these users of anything and everything that happens with their data. Our study focuses on the online privacy policies of Google Chrome browser extensions. We have tried to find the extensions that comply with the data protection guidelines and if all Google Chrome browser extensions are transparent enough to mention the details as per guidelines. Utilizing the power of Natural Language Processing (NLP) techniques, we have employed advanced methodologies to extract insights from these policies.

The transformation of the architects’ vision to architectural form is a lengthy process. From initial sketch to day-to-day life, a design is transformed through the reality of occupation. No matter how much effort is put into a design its final effectiveness is determined by the end user. The access to ample daylight balanced with an adequate sense of visual privacy within ones one home is not often accounted for within the planning process. With current legislation making access to daylight a right within many developed countries, guaranteeing that access within the dense urban environment can mean putting resident’s privacy into question when planning to meet these daylight requirements. Failing to consider the privacy needs of all residents, especially immigrant groups, can lead to privacy driven modifications counterproductive to the overall goal of increasing access to daylight. Resident modifications can, in turn, lead to reductions of daylight levels within the home. There is a need for a system of analysis when it comes to the balance of access to daylight and adequate visual privacy, connecting the critical impacts of these factors on the human physiology and psychology. This proposal puts forward a system to analyze the relationship between the effective light transmission and the perceived visual privacy provided by a given visual privacy solution. The study is based off the analysis of current research regarding the effect of daylight on the human body, the importance of privacy within the home, the impact of cultural background on perception of privacy, and the impact of changing urban density on how people live. The research proposes a system of measurement taking into consideration both the quantitative effective daylight transmittance and a systematic qualitative analysis of perceived visual privacy through participant survey. The data collected would eventually be combined in a way that could be easily communicated to architects, designers, manufacturers and most importantly the end user. This system would be used to ensure that residents are able to effectively balance the level of privacy they require while mitigating the loss of daylight within their homes helping to insure the most benefits for the resident regardless of what home they find themselves in.

The intent of this thesis is to examine the future of the third-party doctrine with the proliferation of technology and the online data we are surrounded with daily, specifically after the United States Supreme Court's decision in Carpenter v. United States. In order to better understand the Supreme Court's reasoning in that case, this thesis will review the history of the third-party doctrine and its roots in United States v. Miller and Smith v. Maryland. A review of Fourth Amendment history and jurisprudence is also crucial to this thesis, as it is imperative that individuals do not forfeit their Constitutional guarantees for the benefit of living in a technologically advanced society. This requires an understanding of the modern-day functional equivalents of "papers" and "effects." Furthermore, this thesis will ultimately answer the following question: Why is it legally significant that we protect at least some data that comes from technologies that our forefathers could have never imagined under the Fourth Amendment? Looking to the future, this thesis will contemplate solutions on how to move forward in this technology era. It will scrutinize the relevancy of the third-party doctrine due to the rise of technology and the enormous amount of information held about us by third parties. In the past, the Third-Party Doctrine may have been good law, but that time has passed. It is time for the Third-Party Doctrine to be abolished so the Fourth Amendment can join the 21st Century.

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Intresset för den personliga hälsan ökar inom samtliga sociala grupper. Människor vill få utökad kontroll över hur sin hälsosituation och de tar till allt fler hjälpmedel för att kunna få bättre svar. Med det digitala samhället nära till hands dyker det upp allt fler tjänster och produkter som agerar hjälpmedel för att produktens användare ska kunna få en större och bättre kontroll över sin hälsa. Produkter och tjänster som gör detta ingår i området Internet of Sports. I samband med att fler användare ansluter sig till dessa tjänster och produkter ökar därmed också datamängden de samlar in. Skyddas denna data i överföringen mellan användaren och företagen och skyddar de som samlar in datan användarens personliga integritet? Uppsatsens syfte är att undersöka detta genom att granska utvalda företag som verkar inom Internet of Sports och se om det går att komma över användarnas personliga data genom man in the middle-attacker. / The interest for personal health is growing in all demographic groups. People want better control regarding their personal health and they are using more aids to get better answers. With the digital society close to hand new products and services are appearing to aid users to get a better knowledge and control of their personal health. The products and services that aim to do this are categorized as Internet of Sports. As more users are signing up for and using these products and services the gathering of data is growing. Is the data that these companies gather safely transfered from the user to the companies and are the companies protecting the user’s privacy? The thesis’ purpose is to examine chosen companies within Internet of Sports and to see if it is possible to access the user’s personal data through man in the middle attacks.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第25427号 / 情博第865号 / 新制||情||145(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 伊藤 孝行, 教授 黒田 知宏, 教授 岡部 寿男, 吉川 正俊(京都大学 名誉教授) / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

<p dir="ltr">Sparse basis recovery is an important learning problem when the number of model dimensions (<i>p</i>) is much larger than the number of samples (<i>n</i>). However, there has been little work that studies sparse basis recovery in the Federated Learning (FL) setting, where the Differential Privacy (DP) of the client data must also be simultaneously protected. Notably, the performance guarantees of existing DP-FL algorithms (such as DP-SGD) will degrade significantly when the system is ill-determined (i.e., <i>p >> n</i>), and thus they will fail to accurately learn the true underlying sparse model. The goal of my thesis is therefore to develop DP-FL sparse basis recovery algorithms that can recover the true underlying sparse basis provably accurately even when <i>p >> n</i>, yet still guaranteeing the differential privacy of the client data.</p><p dir="ltr">During my PhD studies, we developed three DP-FL sparse basis recovery algorithms for this purpose. Our first algorithm, SPriFed-OMP, based on the Orthogonal Matching Pursuit (OMP) algorithm, can achieve high accuracy even when <i>n = O(\sqrt{p})</i> under the stronger Restricted Isometry Property (RIP) assumption for least-square problems. Our second algorithm, Humming-Bird, based on a carefully modified variant of the Forward-Backward Algorithm (FoBA), can achieve differentially private sparse recovery for the same setup while requiring the much weaker Restricted Strong Convexity (RSC) condition. We further extend Humming-Bird to support loss functions beyond least-square satisfying the RSC condition. To the best of our knowledge, these are the first DP-FL results guaranteeing sparse basis recovery in the <i>p >> n</i> setting.</p>

There is a gap between intention and action that people experience when faced with protecting their digital data privacy. Known as the privacy paradox, it is the idea that what a person says they believe (protecting their data privacy is paramount) is not reflective of how they act (relinquishing their data privacy). In other words, what people express about their data privacy is often in opposition to the frequency with which they relinquish their data privacy. The research intends to examine the privacy paradox and consists of two studies, one qualitative and one quantitative. First, focus groups were held, the outcome of which was an attempt at the creation of a typology of words and phrases that consumers use relative to their data privacy. Second, an experiment using Likert scales and Pareto-optimal choice-based conjoint analysis was created based on the typology created in study one, giving insight into what consumers feel are motivators towards protecting or relinquishing their data privacy. The contribution is filling a gap in the existing literature related to the privacy paradox through an analysis of behavior. / Business Administration/Marketing

This study focuses on online news coverage of TikTok's privacy policies to uncover accusations related to security threats between October 2020 and May 2023. Using critical discourse analysis, the research compares TikTok's discourse with other platforms like Facebook and YouTube, highlighting the role of governments, tech companies, and users in shaping this discourse. Furthermore, it demonstrates how cultural and political factors influence privacy discussions, particularly regarding the controversies, discussions, and accusations between the United States and China. AI tool ChatGPT analyzes the discourse by focusing on the news texts' most prominent topics and keywords. The goal is to identify key themes for each highlighted keyword to answer the following questions: (1) How does TikTok's privacy policy construct notions of privacy and security? (2) How do shareholders discuss potential risks in TikTok’s privacy policies? (3) What are the risks of privacy violations and TikTok's global implications? This research asserts that when analyzed alongside the privacy policy discourses of other major social media platforms, TikTok's privacy policies reveal significant implications for cybersecurity, particularly in the context of informed consent. The findings highlight the interplay of cultural, political, and economic factors, emphasizing the urgent need for continuous monitoring and accountability in digital privacy-seeking risk assessment and minimizing.

Made available in DSpace on 2016-06-02T19:06:22Z (GMT). No. of bitstreams: 1 6770.pdf: 4215500 bytes, checksum: 31340bf5dc86076ef8911622315ba83c (MD5) Previous issue date: 2015-02-27 / Financiadora de Estudos e Projetos / The high rate of growth and variety of information available on the Internet can overwhelm users, not leading them to the best decisions. In this context, social recommender systems play an important role on helping users against the effects of information overload. However, these systems need for data collection from its users social context motivates privacy concerns and may discourage its use. Thus, this dissertation presents a privacy negotiation model for social recommender systems to enable user to control his own privacy from the perspective of computer science. So, the user can decide to provide access to their data considering the personalization benefits that the system can offer him in exchange and is not forced to fully accept the privacy policies though. In this model, the privacy control is possible by means of a user interface design pattern using privacy negotiation techniques. The SocialRecSys social recommender system is an implementation of this model that was used in an evaluation with 32 users. The results showed that users are not satisfied with traditional interfaces and the model can better deal with the potentially different privacy preferences of each user. The results also indicated the high usability of the user interfaces of this model, which increase the flexibility of the systems regarding the configuration options of privacy preferences without harm the usage easiness of it. The implementation of this model shows that this is an alternative to reduce the concerns of privacy of social recommender systems users by increasing the flexibility and providing them a better understanding of the recommender systems. So users can feel encouraged to share their data in social recommender systems and take advantage of its personalization benefits. / A alta taxa de crescimento e variedade de informações disponíveis na Internet podem sobrecarregar os usuários, levando-os a não tomar as melhores decisões. Nesse contexto, os sistemas de recomendação social desempenham um importante papel ao auxiliar os usuários contra os efeitos da sobrecarga de informação. No entanto, a necessidade desses sistemas de coletar dados do contexto social dos seus usuários motiva preocupações de privacidade e pode desencorajar o seu uso. Assim, esta dissertação apresenta um modelo de negociação de privacidade para sistemas de recomendação social visando possibilitar ao usuário o controle de sua própria privacidade sob a perspectiva da ciência da computação. Desse modo o usuário pode decidir fornecer acesso aos seus dados considerando os benefícios de personalização que o sistema pode lhe oferecer em troca e ele não é obrigado a aceitar completamente as politicas de privacidade. Nesse modelo, o controle de privacidade é possível por meio de um padrão de projeto de interface de usuário que faz uso de técnicas de negociação de privacidade. O sistema de recomendação social SocialRecSys é uma implementação desse modelo e foi utilizado em uma avaliação com 32 usuários. Os resultados evidenciaram que os usuários não estão satisfeitos com as interfaces tradicionais e que o modelo apresentado pode tratar melhor as potencialmente diferentes preferências de privacidade de cada usuário. Os resultados também indicam a alta usabilidade das interfaces de usuário desse modelo. São interfaces que aumentam a flexibilidade dos sistemas em relação às opções de configuração de preferências de privacidade, sem tornar mais complexo o uso desses sistemas. A implementação do modelo proposto se mostra uma alternativa para reduzir as preocupações com privacidade dos usuários de sistemas de recomendação social, aumentando a flexibilidade e provendo aos usuários maior entendimento desses sistemas. Assim, os usuários podem se sentir encorajados a compartilhar seus dados com os sistemas de recomendação social e desfrutar de seus benefícios de personalização.