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Cited by 247

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Predicting Social Security numbers from public data

Open access
Acquisti, Alessandro, Gross, Ralph·2009·Proceedings of the National Academy of Sciences

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.

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Metadata

Authors
Acquisti, A., & Gross, R.
Venue
Proceedings of the National Academy of Sciences
Published
2009-07-07
Volume
106
Issue
27
Pages
10975-10980
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2000830239
Cited by
247
References
1
FWCI
57.53
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Predicting Social Security numbers from public data. https://app.cochat.ai/papers/10.1073/pnas.0904891106
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Acquisti, A., & Gross, R. (2009). Predicting Social Security numbers from public data. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0904891106

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.