Robust de-anonymization of large sparse datasets

Arvind Narayanan, Vitaly Shmatikov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

970 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2008 IEEE Symposium on Security and Privacy, SP
Pages111-125
Number of pages15
DOIs
StatePublished - Sep 1 2008
Externally publishedYes
Event2008 IEEE Symposium on Security and Privacy, SP - Oakland, CA, United States
Duration: May 18 2008May 21 2008

Publication series

NameProceedings - IEEE Symposium on Security and Privacy

Other

Other2008 IEEE Symposium on Security and Privacy, SP
CountryUnited States
CityOakland, CA
Period5/18/085/21/08

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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    Narayanan, A., & Shmatikov, V. (2008). Robust de-anonymization of large sparse datasets. In Proceedings - 2008 IEEE Symposium on Security and Privacy, SP (pp. 111-125). [4531148] (Proceedings - IEEE Symposium on Security and Privacy). https://doi.org/10.1109/SP.2008.33