ℓ-Diversity: Privacy beyond k-anonymity

Ashwin Machanavajjhala, Johannes Gehrke, Daniel Kifer, Muthuramakrishnan Venkitasubramaniam

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

1283 Scopus citations

Abstract

Publishing data about individuals without revealing sensitive information about them is an important problem. In recent y ears, a new definition of privacy called k-anonymity has gained popularity. In a k-anonymized dataset, each record is indistinguishable from at least k - 1 other records with respect to certain "identifying" attributes. In this paper we show with two simple attacks that a k-anonymized dataset has some subtle, but severe privacy problems. First, we show that an attacker can discover the values of sensitive attributes when there is little diversity in those sensitive attributes. Second, attackers often have background knowledge, and we show that k-anonymity does not guarantee privacy against attackers using background knowledge. We give a detailed analysis of these two attacks and we propose a novel and powerful privacy definition called ℓ-diversity. In addition to building a formal foundation/or ℓ-diversity, we show in an experimental evaluation that ℓ-diversity is practical and can be implemented efficiently.

Original languageEnglish (US)
Title of host publicationProceedings of the 22nd International Conference on Data Engineering, ICDE '06
Number of pages1
DOIs
StatePublished - Oct 17 2006
Event22nd International Conference on Data Engineering, ICDE '06 - Atlanta, GA, United States
Duration: Apr 3 2006Apr 7 2006

Publication series

NameProceedings - International Conference on Data Engineering
Volume2006
ISSN (Print)1084-4627

Other

Other22nd International Conference on Data Engineering, ICDE '06
CountryUnited States
CityAtlanta, GA
Period4/3/064/7/06

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Information Systems

Cite this

Machanavajjhala, A., Gehrke, J., Kifer, D., & Venkitasubramaniam, M. (2006). ℓ-Diversity: Privacy beyond k-anonymity. In Proceedings of the 22nd International Conference on Data Engineering, ICDE '06 [1617392] (Proceedings - International Conference on Data Engineering; Vol. 2006). https://doi.org/10.1109/ICDE.2006.1