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.
Recenzijas
"Big data' - the collection, aggregation or federation, and analysis of vast amounts of increasingly granular data - present[ s] serious challenges not only to personal privacy but also to the tools we use to protect it. Privacy, Big Data, and the Public Good focuses valuable attention on two of these tools: notice and consent, and de-identification - the process of preventing a person's identity from being linked to specific data. [ It] presents a collection of essays from a variety of perspectives, in chapters by some of the heavy hitters in the privacy debate, who make a convincing case that the current framework for dealing with consumer privacy does not adequately address issues posed by big data As society becomes more 'datafied' - a term coined to describe the digital quantification of our existence - our privacy is ever more at risk, especially if we continue to rely on the tools that we employ today to protect it. [ This book] represents a useful and approachable introduction to these important issues.' Science
Papildus informācija
Winner of Choice Outstanding Academic Title 2015.Data access is essential for serving the public good. This book provides new frameworks to address the resultant privacy issues.
| Contributors |
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ix | |
| Editors' Introduction |
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xi | |
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Part I Conceptual Framework |
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1 | (132) |
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1 Monitoring, Datafication, and Consent: Legal Approaches to Privacy in the Big Data Context |
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5 | (39) |
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2 Big Data's End Run around Anonymity and Consent |
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44 | (32) |
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3 The Economics and Behavioral Economics of Privacy |
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76 | (20) |
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4 Changing the Rules: General Principles for Data Use and Analysis |
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96 | (16) |
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5 Enabling Reproducibility in Big Data Research: Balancing Confidentiality and Scientific Transparency |
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112 | (21) |
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Part II Practical Framework |
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133 | (120) |
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6 The Value of Big Data for Urban Science |
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137 | (16) |
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7 Data for the Public Good: Challenges and Barriers in the Context of Cities |
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153 | (20) |
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8 A European Perspective on Research and Big Data Analysis |
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173 | (19) |
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9 The New Deal on Data: A Framework for Institutional Controls |
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192 | (19) |
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10 Engineered Controls for Dealing with Big Data |
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211 | (23) |
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11 Portable Approaches to Informed Consent and Open Data |
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234 | (19) |
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Part III Statistical Framework |
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253 | |
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12 Extracting Information from Big Data: Issues of Measurement, Inference and Linkage |
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257 | (19) |
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13 Using Statistics to Protect Privacy |
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276 | (20) |
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14 Differential Privacy: A Cryptographic Approach to Private Data Analysis |
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296 | |
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Julia Lane is Senior Managing Economist for the American Institutes for Research in Washington, DC. She holds honorary positions as Professor of Economics at the BETA University of Strasbourg CNRS, chercheur associée at Observatoire des Sciences et des Techniques, Paris, and professor at the University of Melbourne's Institute of Applied Economics and Social Research. Victoria Stodden is Assistant Professor of Statistics at Columbia University and is affiliated with the Columbia University Institute for Data Sciences and Engineering. Stefan Bender is head of the Research Data Center (RDC) at the German Federal Employment Agency in the Institute for Employment Research (IAB). Helen Nissenbaum is Professor of Media, Culture, and Communication and Computer Science at New York University, where she is also director of the Information Law Institute.
