(alpha, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
Poster
Paper/Presentation Title | (alpha, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing |
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Presentation Type | Poster |
Authors | Wong, Raymond Chi-Wing (Author), Li, Jiuyong (Author), Fu, Ada Wai-Chee (Author) and Wang, Ke (Author) |
Editors | Eliassi-Rad, Tina, Ungar, Lyle H., Craven, Mark and Gunopulos, Dimitrios |
Journal or Proceedings Title | Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'06) |
Number of Pages | 6 |
Year | 2006 |
Place of Publication | New York, USA |
ISBN | 1595933395 |
Conference/Event | 12th 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. |
Keywords | anonymity, privacy preservation, data publishing, data mining |
ANZSRC Field of Research 2020 | 469999. 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 Affiliations | Chinese University of Hong Kong, China |
Department of Mathematics and Computing | |
Simon Fraser University, Canada |
https://research.usq.edu.au/item/9y0x9/-alpha-k-anonymity-an-enhanced-k-anonymity-model-for-privacy-preserving-data-publishing
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