(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing

  • Authors:
  • Raymond Chi-Wing Wong;Jiuyong Li;Ada Wai-Chee Fu;Ke Wang

  • Affiliations:
  • The Chinese University of Hong Kong;The University of Southern Queensland;The Chinese University of Hong Kong;Simon Fraser University, Canada

  • Venue:
  • Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
  • Year:
  • 2006

Quantified Score

Hi-index 0.01

Visualization

Abstract

Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information. In this paper, we propose an (α, k)-anonymity model to protect both identifications and relationships to sensitive information in data. We discuss the properties of (α, k)-anonymity model. We prove that the optimal (α, k)-anonymity problem is NP-hard. We first presentan optimal global-recoding method for the (α, k)-anonymity problem. Next we propose a local-recoding algorithm which is more scalable and result in less data distortion. The effectiveness and efficiency are shown by experiments. We also describe how the model can be extended to more general case.