Privacy Law Answer Book answers key questions related to the evolving collection, use, and storage of consumers' personal information. The Q&A-formatted guide makes clear sense of the patchwork of federal, state and international laws and regulations, with expert guidance on privacy policies, COPPA, financial privacy, medical privacy, and more. Edited by Jeremy Feigelson (Debevoise & Plimpton LLP), the Answer Book will help readers keep clients and companies one step ahead of the data privacy challenges of tomorrow.

An expert on computer privacy and security shows how we can build privacy into the design of systems from the start. We are tethered to our devices all day, every day, leaving data trails of our searches, posts, clicks, and communications. Meanwhile, governments and businesses collect our data and use it to monitor us without our knowledge. So we have resigned ourselves to the belief that privacy is hard--choosing to believe that websites do not share our information, for example, and declaring that we have nothing to hide anyway. In this informative and illuminating book, a computer privacy and security expert argues that privacy is not that hard if we build it into the design of systems from the start. Along the way, Jaap-Henk Hoepman debunks eight persistent myths surrounding computer privacy. The website that claims it doesn't collect personal data, for example; Hoepman explains that most data is personal, capturing location, preferences, and other information. You don't have anything to hide? There's nothing wrong with wanting to keep personal information--even if it's not incriminating or embarrassing--private. Hoepman shows that just as technology can be used to invade our privacy, it can be used to protect it, when we apply privacy by design. Hoepman suggests technical fixes, discussing pseudonyms, leaky design, encryption, metadata, and the benefits of keeping your data local (on your own device only), and outlines privacy design strategies that system designers can apply now.

The threats to privacy are well known: the National Security Agency tracks our phone calls; Google records where we go online and how we set our thermostats; Facebook changes our privacy settings when it wishes; Target gets hacked and loses control of our credit card information; our medical records are available for sale to strangers; our children are fingerprinted and their every test score saved for posterity; and small robots patrol our schoolyards and drones may soon fill our skies. The contributors to this anthology don't simply describe these problems or warn about the loss of privacy—they propose solutions. They look closely at business practices, public policy, and technology design, and ask, “Should this continue? Is there a better approach?” They take seriously the dictum of Thomas Edison: “What one creates with his hand, he should control with his head.” It's a new approach to the privacy debate, one that assumes privacy is worth protecting, that there are solutions to be found, and that the future is not yet known. This volume will be an essential reference for policy makers and researchers, journalists and scholars, and others looking for answers to one of the biggest challenges of our modern day. The premise is clear: there's a problem—let's find a solution.

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.

Limited legal protections for privacy leave minority communities vulnerable to concrete injuries and violence when their information is exposed. In Privacy at the Margins, Scott Skinner-Thompson highlights why privacy is of acute importance for marginalized groups. He explains how privacy can serve as a form of expressive resistance to government and corporate surveillance regimes - furthering equality goals - and demonstrates why efforts undertaken by vulnerable groups (queer folks, women, and racial and religious minorities) to protect their privacy should be entitled to constitutional protection under the First Amendment and related equality provisions. By examining the ways even limited privacy can enrich and enhance our lives at the margins in material ways, this work shows how privacy can be transformed from a liberal affectation to a legal tool of liberation from oppression.

American essayist and Harper's contributing editor Garret Keizer offers a brilliant, literate look at our strip-searched, over-shared, viral-videoed existence. Body scans at the airport, candid pics on Facebook, a Twitter account for your stray thoughts, and a surveillance camera on every street corner -- today we have an audience for all of the extraordinary and banal events of our lives. The threshold between privacy and exposure becomes more permeable by the minute. But what happens to our private selves when we cannot escape scrutiny, and to our public personas when they pass from our control? In this wide-ranging, penetrating addition to the Big Ideas//Small Books series, and in his own unmistakable voice, Garret Keizer considers the moral dimensions of privacy in relation to issues of social justice, economic inequality, and the increasing commoditization of the global marketplace. Though acutely aware of the digital threat to privacy rights, Keizer refuses to see privacy in purely technological terms or as an essentially legalistic value. Instead, he locates privacy in the human capacity for resistance and in the sustainable society "with liberty and justice for all."

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.

Massive amounts of data on human beings can now be analyzed. Pragmatic purposes abound, including selling goods and services, winning political campaigns, and identifying possible terrorists. Yet 'big data' can also be harnessed to serve the public good: scientists can use big data to do research that improves the lives of human beings, improves government services, and reduces taxpayer costs. In order to achieve this goal, researchers must have access to this data - raising important privacy questions. What are the ethical and legal requirements? What are the rules of engagement? What are the best ways to provide access while also protecting confidentiality? Are there reasonable mechanisms to compensate citizens for privacy loss? The goal of this book is to answer some of these questions. The book's authors paint an intellectual landscape that includes legal, economic, and statistical frameworks. The authors also identify new practical approaches that simultaneously maximize the utility of data access while minimizing information risk.

An Economist Book of the Year Every minute of every day, our data is harvested and exploited… It is time to pull the plug on the surveillance economy. Governments and hundreds of corporations are spying on you, and everyone you know. They're not just selling your data. They're selling the power to influence you and decide for you. Even when you've explicitly asked them not to. Reclaiming privacy is the only way we can regain control of our lives and our societies. These governments and corporations have too much power, and their power stems from us--from our data. Privacy is as collective as it is personal, and it's time to take back control. Privacy Is Power tells you how to do exactly that. It calls for the end of the data economy and proposes concrete measures to bring that end about, offering practical solutions, both for policymakers and ordinary citizens.

Engineer privacy into your systems with these hands-on techniques for data governance, legal compliance, and surviving security audits. In Data Privacy you will learn how to: Classify data based on privacy risk Build technical tools to catalog and discover data in your systems Share data with technical privacy controls to measure reidentification risk Implement technical privacy architectures to delete data Set up technical capabilities for data export to meet legal requirements like Data Subject Asset Requests (DSAR) Establish a technical privacy review process to help accelerate the legal Privacy Impact Assessment (PIA) Design a Consent Management Platform (CMP) to capture user consent Implement security tooling to help optimize privacy Build a holistic program that will get support and funding from the C-Level and board Data Privacy teaches you to design, develop, and measure the effectiveness of privacy programs. You’ll learn from author Nishant Bhajaria, an industry-renowned expert who has overseen privacy at Google, Netflix, and Uber. The terminology and legal requirements of privacy are all explained in clear, jargon-free language. The book’s constant awareness of business requirements will help you balance trade-offs, and ensure your user’s privacy can be improved without spiraling time and resource costs. About the technology Data privacy is essential for any business. Data breaches, vague policies, and poor communication all erode a user’s trust in your applications. You may also face substantial legal consequences for failing to protect user data. Fortunately, there are clear practices and guidelines to keep your data secure and your users happy. About the book Data Privacy: A runbook for engineers teaches you how to navigate the trade-off s between strict data security and real world business needs. In this practical book, you’ll learn how to design and implement privacy programs that are easy to scale and automate. There’s no bureaucratic process—just workable solutions and smart repurposing of existing security tools to help set and achieve your privacy goals. What's inside Classify data based on privacy risk Set up capabilities for data export that meet legal requirements Establish a review process to accelerate privacy impact assessment Design a consent management platform to capture user consent About the reader For engineers and business leaders looking to deliver better privacy. About the author Nishant Bhajaria leads the Technical Privacy and Strategy teams for Uber. His previous roles include head of privacy engineering at Netflix, and data security and privacy at Google. Table of Contents PART 1 PRIVACY, DATA, AND YOUR BUSINESS 1 Privacy engineering: Why it’s needed, how to scale it 2 Understanding data and privacy PART 2 A PROACTIVE PRIVACY PROGRAM: DATA GOVERNANCE 3 Data classification 4 Data inventory 5 Data sharing PART 3 BUILDING TOOLS AND PROCESSES 6 The technical privacy review 7 Data deletion 8 Exporting user data: Data Subject Access Requests PART 4 SECURITY, SCALING, AND STAFFING 9 Building a consent management platform 10 Closing security vulnerabilities 11 Scaling, hiring, and considering regulations