Achieving anonymity via clustering

Gagan Aggarwal*, Tomás Feder, Krishnaram Kenthapadi, Samir Khuller, Rina Panigrahy, Dilys Thomas, An Zhu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

67 Scopus citations


Publishing data for analysis from a table containing personal records, while maintaining individual privacy, is a problem of increasing importance today. The traditional approach of deidentifying records is to remove identifying fields such as social security number, name, etc. However, recent research has shown that a large fraction of the U.S. population can be identified using nonkey attributes (called quasi-identifiers) such as date of birth, gender, and zip code. The κ-anonymity model protects privacy via requiring that nonkey attributes that leak information are suppressed or

Original languageEnglish (US)
Article number49
JournalACM Transactions on Algorithms
Issue number3
StatePublished - Jun 1 2010


  • Anonymity
  • Approximation algorithms
  • Clustering
  • Privacy

ASJC Scopus subject areas

  • Mathematics (miscellaneous)

Fingerprint Dive into the research topics of 'Achieving anonymity via clustering'. Together they form a unique fingerprint.

Cite this