(α, k)-anonymity: An enhanced k-anonymity model for privacy-preserving data publishing
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AbstractPrivacy 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 present an 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 cases. Copyright 2006 ACM.
All Author(s) ListWong R.C.-W., Li J., Fu A.W.-C., Wang K.
Name of ConferenceKDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Start Date of Conference20/08/2006
End Date of Conference23/08/2006
Place of ConferencePhiladelphia, PA
Country/Region of ConferenceUnited States of America
Year2006
Month10
Day16
Volume Number2006
Pages754 - 759
ISBN1595933395
LanguagesEnglish-United Kingdom
KeywordsAnonymity, Data mining, Data publishing, Privacy preservation

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