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Related Experiment Video

Updated: Jun 21, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Predicting Social Security numbers from public data.

Alessandro Acquisti1, Ralph Gross

  • 1Carnegie Mellon University, Pittsburgh, PA 15213, USA. acquisti@andrew.cmu.edu

Proceedings of the National Academy of Sciences of the United States of America
|July 8, 2009
PubMed
Summary
This summary is machine-generated.

Birth date and place can predict Social Security numbers (SSNs). Public data, like the Death Master File and social media, enables statistical inference of SSNs, especially for younger individuals, revealing privacy risks.

Related Experiment Videos

Last Updated: Jun 21, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Computer Science
  • Information Security
  • Data Privacy

Background:

  • Publicly available data, including birth dates and locations, is increasingly accessible.
  • The Social Security Administration's Death Master File is publicly accessible.
  • Data brokers and social networking sites aggregate vast amounts of personal information.

Purpose of the Study:

  • To investigate the correlation between birth data and Social Security numbers (SSNs).
  • To determine if SSNs can be statistically inferred using publicly available information.
  • To quantify the privacy risks associated with modern data economies.

Main Methods:

  • Analysis of correlations between birth dates, places of birth, and SSNs.
  • Utilizing publicly accessible datasets, including the Death Master File.
  • Examining data aggregation practices of data brokers and social media platforms.

Main Results:

  • A significant correlation was found between birth data and SSNs.
  • Statistical inference of SSNs is possible for younger cohorts using public data.
  • The study quantifies the privacy risks stemming from interconnected data sources.

Conclusions:

  • Individuals' birth information can be exploited to predict their SSNs.
  • The convergence of public data sources creates unforeseen privacy vulnerabilities.
  • Urgent attention is needed to address the privacy implications of data aggregation.