Predicting Social Security numbers from public data

被引:116
作者
Acquisti, Alessandro [1 ]
Gross, Ralph [1 ]
机构
[1] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA
基金
美国国家科学基金会; 美国安德鲁·梅隆基金会;
关键词
identity theft; online social networks; privacy; statistical reidentification;
D O I
10.1073/pnas.0904891106
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页码:10975 / 10980
页数:6
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