Robust de-anonymization of large sparse datasets

被引:1189
作者
Narayanan, Arvind [1 ]
Shmatikov, Vitaly [1 ]
机构
[1] Univ Texas Austin, Austin, TX 78712 USA
来源
PROCEEDINGS OF THE 2008 IEEE SYMPOSIUM ON SECURITY AND PRIVACY | 2008年
关键词
D O I
10.1109/SP.2008.33
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
We present a new class of statistical de-anonymization attacks against high-dimensional micro-data, such as individual preferences, recommendations, transaction records and so on. Our techniques are robust to perturbation in the data and tolerate some mistakes in the adversary's background knowledge. We apply our de-anonymization methodology to the Netflix Prize dataset, which contains anonymous movie ratings of 500,000 subscribers of Netflix, the world's largest online movie rental service. We demonstrate that an adversary who knows only a little bit about an individual subscriber can easily identify this subscriber's record in the dataset. Using the Internet Movie Database as the source of background knowledge, we successfully identified the Netflix records of known users, uncovering their apparent political preferences and other potentially sensitive information.
引用
收藏
页码:111 / 125
页数:15
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