title: A game theoretic framework for analyzing re-identification risk.
abstract: Given the potential wealth of insights in personal data the big databases can provide, many organizations aim to share data while protecting privacy by sharing de-identified data, but are concerned because various demonstrations show such data can be re-identified. Yet these investigations focus on how attacks can be perpetrated, not the likelihood they will be realized. This paper introduces a game theoretic framework that enables a publisher to balance re-identification risk with the value of sharing data, leveraging a natural assumption that a recipient only attempts re-identification if its potential gains outweigh the costs. We apply the framework to a real case study, where the value of the data to the publisher is the actual grant funding dollar amounts from a national sponsor and the re-identification gain of the recipient is the fine paid to a regulator for violation of federal privacy rules. There are three notable findings: 1) it is possible to achieve zero risk, in that the recipient never gains from re-identification, while sharing almost as much data as the optimal solution that allows for a small amount of risk; 2) the zero-risk solution enables sharing much more data than a commonly invoked de-identification policy of the U.S. Health Insurance Portability and Accountability Act (HIPAA); and 3) a sensitivity analysis demonstrates these findings are robust to order-of-magnitude changes in player losses and gains. In combination, these findings provide support that such a framework can enable pragmatic policy decisions about de-identified data sharing.
authors: Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America.
authors: Zhiyu
authors: Z
authors: Wan
authors: Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America.
authors: Yevgeniy
authors: Y
authors: Vorobeychik
authors: Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America.
authors: Weiyi
authors: W
authors: Xia
authors: Center for Biomedical Ethics and Society, Vanderbilt University, Tennessee, United States of America.
authors: Ellen Wright
authors: EW
authors: Clayton
authors: Department of Computer Science, University of Texas at Dallas, Richardson, Texas, United States of America.
authors: Murat
authors: M
authors: Kantarcioglu
authors: Department of Computer Science, University of Texas at Dallas, Richardson, Texas, United States of America.
authors: Ranjit
authors: R
authors: Ganta
authors: Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, United States of America.
authors: Raymond
authors: R
authors: Heatherly
authors: Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, United States of America.
authors: Bradley A
authors: BA
authors: Malin
mesh_terms: D016208:Databases, Factual; D020408:Health Insurance Portability and Accountability Act; D006801:Humans; D033181:Information Dissemination; D008962:Models, Theoretical; D018907:Privacy; D012306:Risk; D057386:Search Engine; D014481:United States