In exploring a series of problems associated with privacy and the First Amendment, Bloustein defines individual and group privacy, distinguishing them from each other and related concepts. He also identifies the public interest in individual privacy as individual integrity or liberty, and that of group privacy as the integrity of social structure. The legal protection afforded each of these forms of privacy is illustrated at length, as is the clash between them and the constitutional guarantees of the First Amendment and the citizen's general right to know. In his final essay, Bloustein insists that the concept of group privacy is essential to a properly functioning social structure, and warns that it would be disastrous if this principle were neglected as part of an overreaction to the misuse of group confidences that characterized the Nixon era.

The goal of the book is to present the latest research on the new challenges of data technologies. It will offer an overview of the social, ethical and legal problems posed by group profiling, big data and predictive analysis and of the different approaches and methods that can be used to address them. In doing so, it will help the reader to gain a better grasp of the ethical and legal conundrums posed by group profiling. The volume first maps the current and emerging uses of new data technologies and clarifies the promises and dangers of group profiling in real life situations. It then balances this with an analysis of how far the current legal paradigm grants group rights to privacy and data protection, and discusses possible routes to addressing these problems. Finally, an afterword gathers the conclusions reached by the different authors and discuss future perspectives on regulating new data technologies.

This open access book provides researchers and professionals with a foundational understanding of online privacy as well as insight into the socio-technical privacy issues that are most pertinent to modern information systems, covering several modern topics (e.g., privacy in social media, IoT) and underexplored areas (e.g., privacy accessibility, privacy for vulnerable populations, cross-cultural privacy). The book is structured in four parts, which follow after an introduction to privacy on both a technical and social level: Privacy Theory and Methods covers a range of theoretical lenses through which one can view the concept of privacy. The chapters in this part relate to modern privacy phenomena, thus emphasizing its relevance to our digital, networked lives. Next, Domains covers a number of areas in which privacy concerns and implications are particularly salient, including among others social media, healthcare, smart cities, wearable IT, and trackers. The Audiences section then highlights audiences that have traditionally been ignored when creating privacy-preserving experiences: people from other (non-Western) cultures, people with accessibility needs, adolescents, and people who are underrepresented in terms of their race, class, gender or sexual identity, religion or some combination. Finally, the chapters in Moving Forward outline approaches to privacy that move beyond one-size-fits-all solutions, explore ethical considerations, and describe the regulatory landscape that governs privacy through laws and policies. Perhaps even more so than the other chapters in this book, these chapters are forward-looking by using current personalized, ethical and legal approaches as a starting point for re-conceptualizations of privacy to serve the modern technological landscape. The book's primary goal is to inform IT students, researchers, and professionals about both the fundamentals of online privacy and the issues that are most pertinent to modern information systems. Lecturers or teachers can assign (parts of) the book for a “professional issues” course. IT professionals may select chapters covering domains and audiences relevant to their field of work, as well as the Moving Forward chapters that cover ethical and legal aspects. Academics who are interested in studying privacy or privacy-related topics will find a broad introduction in both technical and social aspects.

Health and fitness apps collect various personal information including name, email address, age, height, weight, and in some cases, detailed health information. When using these apps, many users trustfully log everything from diet to sleep patterns. However, by sharing such personal information, end-users may make themselves targets to misuse of this information by unknown third parties, such as insurance companies. Despite the important role of informed consent in the creation of health and fitness applications, the intersection of ethics and information sharing is understudied and is an often-ignored topic during the creation of mobile applications. Privacy Concerns Surrounding Personal Information Sharing on Health and Fitness Mobile Apps is a key reference source that provides research on the dangers of sharing personal information on health and wellness apps, as well as how such information can be used by employers, insurance companies, advertisers, and other third parties. While highlighting topics such as data ethics, privacy management, and information sharing, this publication explores the intersection of ethics and privacy using various quantitative, qualitative, and critical analytic approaches. It is ideally designed for policymakers, software developers, mobile app designers, legal specialists, privacy analysts, data scientists, researchers, academicians, and upper-level students.

The essays selected for this volume reflect the many paths followed to develop a new, more robust methodology (idMAPPING) for investigating privacy. Each article deals with the three dimensions of time, space and place by addressing a number of questions such as: who? Which individual? When? How? Is privacy viewed from the perspective of legal theory, or of information science? Or from the viewpoint of sociology, social psychology, philosophy, information ethics or data protection law? The reader is offered a multi-disciplinary overview of the subject, a mosaic made up of several snapshots taken at different times by different scholars with different points of view. The detailed introduction increases clarity in parts of the picture where the way that the pieces fit together may not be immediately apparent, and concludes by challenging internet-era fallacies. Taken together, the articles demonstrate an innovative approach to evidence-based policy-making, and show privacy scholarship at its best.

The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition. The Algorithmic Foundations of Differential Privacy starts out by motivating and discussing the meaning of differential privacy, and proceeds to explore the fundamental techniques for achieving differential privacy, and the application of these techniques in creative combinations, using the query-release problem as an ongoing example. A key point is that, by rethinking the computational goal, one can often obtain far better results than would be achieved by methodically replacing each step of a non-private computation with a differentially private implementation. Despite some powerful computational results, there are still fundamental limitations. Virtually all the algorithms discussed herein maintain differential privacy against adversaries of arbitrary computational power -- certain algorithms are computationally intensive, others are efficient. Computational complexity for the adversary and the algorithm are both discussed. The monograph then turns from fundamentals to applications other than query-release, discussing differentially private methods for mechanism design and machine learning. The vast majority of the literature on differentially private algorithms considers a single, static, database that is subject to many analyses. Differential privacy in other models, including distributed databases and computations on data streams, is discussed. The Algorithmic Foundations of Differential Privacy is meant as a thorough introduction to the problems and techniques of differential privacy, and is an invaluable reference for anyone with an interest in the topic.