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.

Edward J. Bloustein was the president of Rutgers University, and a distinguished scholar of the law. The four essays on privacy that comprise this book were completed over a thirteen-year period, and the development of the author's thinking parallels increasing thoughtful concern about privacy in the larger society. This development is especially appropriate to discussions of privacy and the "right to know" in the current era. The author analyzes individual and group privacy as legal concepts and examines the relationship of each to the legal right of the public to be informed about, and of a publisher to publish, private or confidential information. 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 new opening by Nathaniel Pallone provides a fresh context for evaluating the intellectual as well as organizational contribution of Bloustein.

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.

Every day, Internet users interact with technologies designed to undermine their privacy. Social media apps, surveillance technologies, and the Internet of Things are all built in ways that make it hard to guard personal information. And the law says this is okay because it is up to users to protect themselves—even when the odds are deliberately stacked against them. In Privacy’s Blueprint, Woodrow Hartzog pushes back against this state of affairs, arguing that the law should require software and hardware makers to respect privacy in the design of their products. Current legal doctrine treats technology as though it were value-neutral: only the user decides whether it functions for good or ill. But this is not so. As Hartzog explains, popular digital tools are designed to expose people and manipulate users into disclosing personal information. Against the often self-serving optimism of Silicon Valley and the inertia of tech evangelism, Hartzog contends that privacy gains will come from better rules for products, not users. The current model of regulating use fosters exploitation. Privacy’s Blueprint aims to correct this by developing the theoretical underpinnings of a new kind of privacy law responsive to the way people actually perceive and use digital technologies. The law can demand encryption. It can prohibit malicious interfaces that deceive users and leave them vulnerable. It can require safeguards against abuses of biometric surveillance. It can, in short, make the technology itself worthy of our trust.

This book constitutes the refereed proceedings of the Workshop on E-Business (WeB 2015), held in Fort Worth, Texas, USA, on December 12, 2015. The theme of WeB 2015 was “Leveraging Service Computing and Big Data Analytics for E-Commerce”, and thus the workshop provided an interactive forum by bringing together researchers and practitioners from all over the world to explore the latest challenges of next-generation e-Business systems and the potential of service computing and big data analytics. The 11 full and 17 short papers, which were selected from 45 submissions to the workshop, addressed a broad coverage of technical, managerial, economic, and strategic issues related to e-business, with emphasis on service computing and big data analytics. They employed various IS research methods such as case study, survey, analytical modeling, experiments, computational models, and design science.

When data from all aspects of our lives can be relevant to our health - from our habits at the grocery store and our Google searches to our FitBit data and our medical records - can we really differentiate between big data and health big data? Will health big data be used for good, such as to improve drug safety, or ill, as in insurance discrimination? Will it disrupt health care (and the health care system) as we know it? Will it be possible to protect our health privacy? What barriers will there be to collecting and utilizing health big data? What role should law play, and what ethical concerns may arise? This timely, groundbreaking volume explores these questions and more from a variety of perspectives, examining how law promotes or discourages the use of big data in the health care sphere, and also what we can learn from other sectors.

This book identifies the principles that should be applied when processing Big Data in the context of food safety risk assessments. Food safety is a critical goal in the protection of individuals’ right to health and the flourishing of the food and feed market. Big Data is fostering new applications capable of enhancing the accuracy of food safety risk assessments. An extraordinary amount of information is analysed to detect the existence or predict the likelihood of future risks, also by means of machine learning algorithms. Big Data and novel analysis techniques are topics of growing interest for food safety agencies, including the European Food Safety Authority (EFSA). This wealth of information brings with it both opportunities and risks concerning the extraction of meaningful inferences from data. However, conflicting interests and tensions among the parties involved are hindering efforts to find shared methods for steering the processing of Big Data in a sound, transparent and trustworthy way. While consumers call for more transparency, food business operators tend to be reluctant to share informational assets. This has resulted in a considerable lack of trust in the EU food safety system. A recent legislative reform, supported by new legal cases, aims to restore confidence in the risk analysis system by reshaping the meaning of data ownership in this domain. While this regulatory approach is being established, breakthrough analytics techniques are encouraging thinking about the next steps in managing food safety data in the age of machine learning. The book focuses on two core topics – data ownership and data governance – by evaluating how the regulatory framework addresses the challenges raised by Big Data and its analysis in an applied, significant, and overlooked domain. To do so, it adopts an interdisciplinary approach that considers both the technological advances and the policy tools adopted in the European Union, while also assuming an ethical perspective when exploring potential solutions. The conclusion puts forward a proposal: an ethical blueprint for identifying the principles – Security, Accountability, Fairness, Explainability, Transparency and Privacy – to be observed when processing Big Data for food safety purposes, including by means of machine learning. Possible implementations are then discussed, also in connection with two recent legislative proposals, namely the Data Governance Act and the Artificial Intelligence Act.