Predicting Social Security Numbers from Public Data

Proceedings of the National Academy of Sciences, 106(27), 10975--10980 (2009)

6 Pages Posted: 6 Jan 2019

See all articles by Alessandro Acquisti

Alessandro Acquisti

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management

Ralph Gross

Carnegie Mellon University

Date Written: 2009

Abstract

Information about an individual’s place and date of birth can be exploited to predict his or her Social Security number (SSN). Using only publicly available information, we observed a correlation between individuals’ SSNs and their birth data and found that for younger cohorts the correlation allows statistical inference of private SSNs. The inferences are made possible by the public availability of the Social Security Administration’s Death Master File and the widespread accessibility of personal information from multiple sources, such as data brokers or profiles on social networking sites. Our results highlight the unexpected privacy consequences of the complex interactions among multiple data sources in modern information economies and quantify privacy risks associated with information revelation in public forums.

Keywords: identity theft, online social networks, privacy, statistical reidentification

Suggested Citation

Acquisti, Alessandro and Gross, Ralph, Predicting Social Security Numbers from Public Data (2009). Proceedings of the National Academy of Sciences, 106(27), 10975--10980 (2009), Available at SSRN: https://ssrn.com/abstract=3305360

Alessandro Acquisti (Contact Author)

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management ( email )

Pittsburgh, PA 15213-3890
United States
412-268-9853 (Phone)
412-268-5339 (Fax)

Ralph Gross

Carnegie Mellon University ( email )

Pittsburgh, PA 15213-3890
United States

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