Differential Privacy: A Primer for a Non-Technical Audience

69 Pages Posted: 25 Mar 2019

See all articles by Alexandra Wood

Alexandra Wood

Harvard University - Berkman Klein Center for Internet & Society

Micah Altman

Massachusetts Institute of Technology (MIT) Libraries; The Brookings Institution

Aaron Bembenek

Harvard University

Mark Bun

Simons Institute for the Theory of Computing; Department of Computer Science, Boston University

Marco Gaboardi

University at Buffalo, SUNY

James Honaker

Pennsylvania State University

Kobbi Nissim

Ben-Gurion University of the Negev

David O'Brien

Harvard University - Berkman Klein Center for Internet & Society

Thomas Steinke

Harvard University

Salil Vadhan

Harvard University - Center for Research on Computation and Society

Date Written: 2018

Abstract

Differential privacy is a formal mathematical framework for quantifying and managing privacy risks. It provides provable privacy protection against a wide range of potential attacks, including those currently unforeseen. Differential privacy is primarily studied in the context of the collection, analysis, and release of aggregate statistics. These range from simple statistical estimations, such as averages, to machine learning. Tools for differentially private analysis are now in early stages of implementation and use across a variety of academic, industry, and government settings. Interest in the concept is growing among potential users of the tools, as well as within legal and policy communities, as it holds promise as a potential approach to satisfying legal requirements for privacy protection when handling personal information. In particular, differential privacy may be seen as a technical solution for analyzing and sharing data while protecting the privacy of individuals in accordance with existing legal or policy requirements for de-identification or disclosure limitation.

This primer seeks to introduce the concept of differential privacy and its privacy implications to non-technical audiences. It provides a simplified and informal, but mathematically accurate, description of differential privacy. Using intuitive illustrations and limited mathematical formalism, it discusses the definition of differential privacy, how differential privacy addresses privacy risks, how differentially private analyses are constructed, and how such analyses can be used in practice. A series of illustrations is used to show how practitioners and policymakers can conceptualize the guarantees provided by differential privacy. These illustrations are also used to explain related concepts, such as composition (the accumulation of risk across multiple analyses), privacy loss parameters, and privacy budgets. This primer aims to provide a foundation that can guide future decisions when analyzing and sharing statistical data about individuals, informing individuals about the privacy protection they will be afforded, and designing policies and regulations for robust privacy protection.

Suggested Citation

Wood, Alexandra and Altman, Micah and Bembenek, Aaron and Bun, Mark and Gaboardi, Marco and Honaker, James and Nissim, Kobbi and O'Brien, David and Steinke, Thomas and Vadhan, Salil, Differential Privacy: A Primer for a Non-Technical Audience (2018). Vanderbilt Journal of Entertainment & Technology Law, Vol. 21, No. 17, 2018; Berkman Klein Center Research Publication No. 2019-2. Available at SSRN: https://ssrn.com/abstract=3338027 or http://dx.doi.org/10.2139/ssrn.3338027

Alexandra Wood (Contact Author)

Harvard University - Berkman Klein Center for Internet & Society ( email )

Harvard Law School
23 Everett, 2nd Floor
Cambridge, MA 02138
United States

Micah Altman

Massachusetts Institute of Technology (MIT) Libraries ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

HOME PAGE: http://informatics.mit.edu

The Brookings Institution ( email )

1775 Massachusetts Ave, NW
Washington, DC 20036
United States

HOME PAGE: http://informatics.mit.edu

Aaron Bembenek

Harvard University ( email )

33 Oxford St
Cambridge, MA 02138
United States

Mark Bun

Simons Institute for the Theory of Computing ( email )

121 Calvin Lab #2190
UC Berkeley
Berkeley, CA 94720
United States

Department of Computer Science, Boston University ( email )

Boston, MA
United States

Marco Gaboardi

University at Buffalo, SUNY ( email )

12 Capen Hall
Buffalo, NY 14260
United States

HOME PAGE: http://www.buffalo.edu/~gaboardi/

James Honaker

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

Kobbi Nissim

Ben-Gurion University of the Negev ( email )

1 Ben-Gurion Blvd
Beer-Sheba 84105, 84105
Israel

David O'Brien

Harvard University - Berkman Klein Center for Internet & Society ( email )

Harvard Law School
23 Everett, 2nd Floor
Cambridge, MA 02138
United States

Thomas Steinke

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

Salil Vadhan

Harvard University - Center for Research on Computation and Society ( email )

33 Oxford Street
Cambridge, MA 02138
United States

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