Robust De-anonymization of Large Sparse Datasets

Narayanan, A., & Shmatikov, V. (2008). Robust De-anonymization of Large Sparse Datasets. 2008 IEEE Symposium on Security and Privacy, 111-125.

Type

Conference paper

Intellectual base

Primary research

Year

2008

Pages

111-125

DOI

10.1109/sp.2008.33

DOI registrant

Institute of Electrical and Electronics Engineers

Link

https://doi.org/10.1109/SP.2008.33

Cited by

2 citations across 2 artifacts.

Each citation links to the sentence it supports in the citing article.

Domain-specific

• Mosaic Effect
  • Narayanan and Shmatikov demonstrated robust de-anonymization of a large sparse ratings dataset using limited external information, showing why anonymity cannot always be assessed inside one table.

Mechanisms

• Linkage Attack Test
  • Its strength is that it turns "we removed the identifiers" into a measured claim, catching the quasi-identifier exposure that field-level redaction structurally misses.

    This sourceMeasures de-anonymization success and shows that sparse attribute combinations and auxiliary data can re-identify records after direct identifiers are removed.

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