Overview

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 ...
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Privacy, Big Data, and the Public Good: Frameworks for Engagement

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Overview

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.
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Product Details

  • ISBN-13: 9781316093863
  • Publisher: Cambridge University Press
  • Publication date: 6/30/2014
  • Sold by: Barnes & Noble
  • Format: eBook
  • Sales rank: 1,190,209
  • File size: 2 MB

Meet the Author

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 associƩe 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.

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Table of Contents

Part I. Conceptual Framework: Editors' introduction Julia Lane, Victoria Stodden, Stefan Bender and Helen Nissenbaum; 1. Monitoring, datafication, and consent: legal approaches to privacy in the big data context Katherine J. Strandburg; 2. Big data's end run around anonymity and consent Solon Barocas and Helen Nissenbaum; 3. The economics and behavioral economics of privacy Alessandro Acquisti; 4. The legal and regulatory framework: what do the rules say about data analysis? Paul Ohm; 5. Enabling reproducibility in big data research: balancing confidentiality and scientific transparency Victoria Stodden; Part II. Practical Framework: Editors' introduction Julia Lane, Victoria Stodden, Stefan Bender and Helen Nissenbaum; 6. The value of big data for urban science Steven E. Koonin and Michael J. Holland; 7. The new role of cities in creating value Robert Goerge; 8. A European perspective Peter Elias; 9. Institutional controls: the new deal on data Daniel Greenwood, Arkadiusz Stopczynski, Brian Sweatt, Thomas Hardjono and Alex Pentland; 10. The operational framework: engineered controls Carl Landwehr; 11. Portable approaches to informed consent and open data John Wilbanks; Part III. Statistical Framework: Editors' introduction Julia Lane, Victoria Stodden, Stefan Bender and Helen Nissenbaum; 12. Extracting information from big data Frauke Kreuter and Roger Peng; 13. Using statistics to protect privacy Alan F. Karr and Jerome P. Reiter; 14. Differential privacy: a cryptographic approach to private data analysis Cynthia Dwork.
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