Predicting Social Security numbers from public data

Type Journal Article Author Alessandro Acquisti Author Ralph Gross URL http://www.pnas.org/content/106/27/10975 Volume 106 Issue 27 Pages 10975-10980 Publication Proceedings of the National Academy of Sciences ISSN 0027-8424, 1091-6490 Date 07/07/2009 Extra PMID: 19581585 Journal Abbr PNAS DOI 10.1073/pnas.0904891106 Accessed 2016-07-19 21:55:17 Library Catalog www.pnas.org Language en 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.