期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 106, 期 27, 页码 10975-10980出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.0904891106
关键词
identity theft; online social networks; privacy; statistical reidentification
资金
- National Science Foundation [0713361]
- U.S. Army Research Office [DAAD190210389]
- Carnegie Mellon Berkman Fund
- Pittsburgh Supercomputing Center
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [0713361] Funding Source: National Science Foundation
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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