A privacy-enhanced microaggregation method

Yingjiu Li, Sencun Zhu, Lingyu Wang, Sushil Jajodia

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

19 Scopus citations

Abstract

Microaggregation is a statistical disclosure control technique for protecting microdata (i.e., individual records), which are important products of statistical offices. The basic idea of microaggregation is to cluster individual records in microdata into a number of mutually exclusive groups prior to publication, and then publish the average over each group instead of individual records. Previous methods require fixed or variable group size in clustering in order to reduce information loss. However, the security aspect of microaggregation has not been extensively studied. We argue that the group size requirement is not enough for protecting the privacy of microdata. We propose a new microaggregation method, which we call secure-k-Ward, to enhance the individual’s privacy. Our method, which is optimization based, minimizes information loss and overall mean deviation while at the same time guarantees that the security requirement for protecting the microdata is satisfied.

Original languageEnglish (US)
Title of host publicationFoundations of Information and Knowledge Systems - 2nd International Symposium, FoIKS 2002, Proceedings
EditorsThomas Eiter, Klaus-Dieter Schewe
PublisherSpringer Verlag
Pages148-159
Number of pages12
ISBN (Print)3540432205, 9783540432203
DOIs
StatePublished - Jan 1 2002
Event2nd International Symposium on Foundations of Information and Knowledge Systems, FoIKS 2002 - Salzau Castle, Germany
Duration: Feb 20 2002Feb 23 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2284
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Symposium on Foundations of Information and Knowledge Systems, FoIKS 2002
CountryGermany
CitySalzau Castle
Period2/20/022/23/02

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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