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

Poster

Wong, Raymond Chi-Wing, Li, Jiuyong, Fu, Ada Wai-Chee and Wang, Ke. 2006. "(alpha, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing." Eliassi-Rad, Tina, Ungar, Lyle H., Craven, Mark and Gunopulos, Dimitrios (ed.) 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'06). Philadelphia, USA 20 - 23 Aug 2006 New York, USA.
Paper/Presentation Title

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

Presentation TypePoster
AuthorsWong, Raymond Chi-Wing (Author), Li, Jiuyong (Author), Fu, Ada Wai-Chee (Author) and Wang, Ke (Author)
EditorsEliassi-Rad, Tina, Ungar, Lyle H., Craven, Mark and Gunopulos, Dimitrios
Journal or Proceedings TitleProceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'06)
Number of Pages6
Year2006
Place of PublicationNew York, USA
ISBN1595933395
Conference/Event12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'06)
Event Details
12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'06)
Event Date
20 to end of 23 Aug 2006
Event Location
Philadelphia, USA
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 (alpha, k)-anonymity model to protect both identifications and relationships to sensitive information in data. We discuss the properties of (alpha, k)-anonymity model. We prove that the optimal (alpha, k)- anonymity problem is NP-hard. We first present an optimal global recoding method for the (alpha, 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 cases.

Keywordsanonymity, privacy preservation, data publishing, data mining
ANZSRC Field of Research 2020469999. Other information and computing sciences not elsewhere classified
461305. Data structures and algorithms
Public Notes

File reproduced in accordance with the copyright policy of the publisher/author.

Byline AffiliationsChinese University of Hong Kong, China
Department of Mathematics and Computing
Simon Fraser University, Canada
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