Understanding Privacy

Privacy is one of the most important concepts of our time, yet it is also one of the most elusive. As rapidly changing technology makes information increasingly available, scholars, activists, and policymakers have struggled to define privacy, with many conceding that the task is virtually impossible. In this concise and lucid book, Daniel J. Solove offers a comprehensive overview of the difficulties involved in discussions of privacy and ultimately provides a provocative resolution. He argues that no single definition can be workable, but rather that there are multiple forms of privacy, related to one another by family resemblances. His theory bridges cultural differences and addresses historical changes in views on privacy. Drawing on a broad array of interdisciplinary sources, Solove sets forth a framework for understanding privacy that provides clear, practical guidance for engaging with relevant issues. Understanding Privacy will be an essential introduction to long-standing debates and an invaluable resource for crafting laws and policies about surveillance, data mining, identity theft, state involvement in reproductive and marital decisions, and other pressing contemporary matters concerning privacy.

The Comprehensive Guide to Engineering and Implementing Privacy Best Practices As systems grow more complex and cybersecurity attacks more relentless, safeguarding privacy is ever more challenging. Organizations are increasingly responding in two ways, and both are mandated by key standards such as GDPR and ISO/IEC 27701:2019. The first approach, privacy by design, aims to embed privacy throughout the design and architecture of IT systems and business practices. The second, privacy engineering, encompasses the technical capabilities and management processes needed to implement, deploy, and operate privacy features and controls in working systems. In Information Privacy Engineering and Privacy by Design, internationally renowned IT consultant and author William Stallings brings together the comprehensive knowledge privacy executives and engineers need to apply both approaches. Using the techniques he presents, IT leaders and technical professionals can systematically anticipate and respond to a wide spectrum of privacy requirements, threats, and vulnerabilities—addressing regulations, contractual commitments, organizational policies, and the expectations of their key stakeholders. • Review privacy-related essentials of information security and cryptography • Understand the concepts of privacy by design and privacy engineering • Use modern system access controls and security countermeasures to partially satisfy privacy requirements • Enforce database privacy via anonymization and de-identification • Prevent data losses and breaches • Address privacy issues related to cloud computing and IoT • Establish effective information privacy management, from governance and culture to audits and impact assessment • Respond to key privacy rules including GDPR, U.S. federal law, and the California Consumer Privacy Act This guide will be an indispensable resource for anyone with privacy responsibilities in any organization, and for all students studying the privacy aspects of cybersecurity.

Cybersecurity, data privacy law, and the related legal implications overlap into a relevant and developing area in the legal field. However, many legal practitioners lack the foundational understanding of computer processes which are fundamental for applying existing and developing legal structures to the issue of cybersecurity and data privacy. At the same time, those who work and research in cybersecurity are often unprepared and unaware of the nuances of legal application. This book translates the fundamental building blocks of data privacy and (cyber)security law into basic knowledge that is equally accessible and educational for those working and researching in either field, those who are involved with businesses and organizations, and the general public.

This textbook provides a unique lens through which the myriad of existing Privacy Enhancing Technologies (PETs) can be easily comprehended and appreciated. It answers key privacy-centered questions with clear and detailed explanations. Why is privacy important? How and why is your privacy being eroded and what risks can this pose for you? What are some tools for protecting your privacy in online environments? How can these tools be understood, compared, and evaluated? What steps can you take to gain more control over your personal data? This book addresses the above questions by focusing on three fundamental elements: It introduces a simple classification of PETs that allows their similarities and differences to be highlighted and analyzed; It describes several specific PETs in each class, including both foundational technologies and important recent additions to the field; It explains how to use this classification to determine which privacy goals are actually achievable in a given real-world environment. Once the goals are known, this allows the most appropriate PETs to be selected in order to add the desired privacy protection to the target environment. To illustrate, the book examines the use of PETs in conjunction with various security technologies, with the legal infrastructure, and with communication and computing technologies such as Software Defined Networking (SDN) and Machine Learning (ML). Designed as an introductory textbook on PETs, this book is essential reading for graduate-level students in computer science and related fields, prospective PETs researchers, privacy advocates, and anyone interested in technologies to protect privacy in online environments.

Privacy is one of the most urgent issues associated with information technology and digital media. This book claims that what people really care about when they complain and protest that privacy has been violated is not the act of sharing information itself—most people understand that this is crucial to social life —but the inappropriate, improper sharing of information. Arguing that privacy concerns should not be limited solely to concern about control over personal information, Helen Nissenbaum counters that information ought to be distributed and protected according to norms governing distinct social contexts—whether it be workplace, health care, schools, or among family and friends. She warns that basic distinctions between public and private, informing many current privacy policies, in fact obscure more than they clarify. In truth, contemporary information systems should alarm us only when they function without regard for social norms and values, and thereby weaken the fabric of social life.

Proposes a new way of thinking about information privacy that leverages law to protect disclosures in contexts of trust.

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.