XColor: Protecting general proximity privacy

Ting Wang, Ling Liu

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

4 Scopus citations


As a severe threat in anonymized data publication, proximity breach is gaining increasing attention. Such breach occurs when an attacker learns with high confidence that the sensitive information of a victim associates with a set of semantically proximate values, even though not sure about the exact one. Recently (ε, δ)-dissimilarity [14] has been proposed as an effective countermeasure against general proximity attack. In this paper, we present a detailed analytical study on the fulfillment of this principle, derive criteria to efficiently test its satisfiability for given microdata, and point to a novel anonymization model, XCOLOR, with theoretical guarantees on both operation efficiency and utility preservation.

Original languageEnglish (US)
Title of host publication26th IEEE International Conference on Data Engineering, ICDE 2010 - Conference Proceedings
Number of pages4
StatePublished - Jun 1 2010
Event26th IEEE International Conference on Data Engineering, ICDE 2010 - Long Beach, CA, United States
Duration: Mar 1 2010Mar 6 2010

Publication series

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


Other26th IEEE International Conference on Data Engineering, ICDE 2010
Country/TerritoryUnited States
CityLong Beach, CA

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Information Systems

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