Data Privacy against Composition Attack

  • Muzammil M. Baig
  • Jiuyong Li
  • Jixue Liu
  • Xiaofeng Ding
  • Hua Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7238)


Data anonymization has become a major technique in privacy preserving data publishing. Many methods have been proposed to anonymize one dataset and a series of datasets of a data holder. However, no method has been proposed for the anonymization scenario of multiple independent data publishing. A data holder publishes a dataset, which contains overlapping population with other datasets published by other independent data holders. No existing methods are able to protect privacy in such multiple independent data publishing. In this paper we propose a new generalization principle (ρ,α)-anonymization that effectively overcomes the privacy concerns for multiple independent data publishing. We also develop an effective algorithm to achieve the (ρ,α)-anonymization. We experimentally show that the proposed algorithm anonymizes data to satisfy the privacy requirement and preserves high quality data utility.


Data anonymity privacy composition attack 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Muzammil M. Baig
    • 1
  • Jiuyong Li
    • 1
  • Jixue Liu
    • 1
  • Xiaofeng Ding
    • 1
  • Hua Wang
    • 2
  1. 1.School of Computer and Information ScienceUniversity of South AustraliaMawson LakesAustralia
  2. 2.Department of Maths & ComputingUniversity of Southern QueenslandToowoombaAustralia

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