Privacy, Big Data, and the Public Good
Privacy, Big Data, and the Public Good
Victoria Stodden , Columbia University, New York
Stefan Bender , Institute for Employment Research of the German Federal Employment Agency
Helen Nissenbaum , New York University
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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.
- Structured in three easy-to-understand sections
- Written in a very accessible format
- Touches upon three crucial areas: privacy, data access and big data
Reviews & endorsements
"'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
Product details
May 2014Adobe eBook Reader
9781316094167
0 pages
0kg
4 b/w illus.
This ISBN is for an eBook version which is distributed on our behalf by a third party.
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.
