| On the complexity of optimal K-anonymity |
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Symposium on Principles of Database Systems
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Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
table of contents
Paris, France
SESSION: Data exchange II
table of contents
Pages: 223 - 228
Year of Publication: 2004
ISBN:158113858X
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Downloads (6 Weeks): 13, Downloads (12 Months): 95, Citation Count: 69
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ABSTRACT
The technique of k-anonymization has been proposed in the literature as an alternative way to release public information, while ensuring both data privacy and data integrity. We prove that two general versions of optimal k-anonymization of relations are NP-hard, including the suppression version which amounts to choosing a minimum number of entries to delete from the relation. We also present a polynomial time algorithm for optimal k-anonymity that achieves an approximation ratio independent of the size of the database, when k is constant. In particular, it is a O(k log k)-approximation where the constant in the big-O is no more than 4, However, the runtime of the algorithm is exponential in k. A slightly more clever algorithm removes this condition, but is a O(k log m)-approximation, where m is the degree of the relation. We believe this algorithm could potentially be quite fast in practice.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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[doi> 10.1145/335168.335210]
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L. Sweeney. Optimal anonymity using k-similar, a new clustering algorithm. Under review, 2003.
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CITED BY 69
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Raymond Chi-Wing , Jiuyong Li , Ada Wai-Chee Fu , Ke Wang, (α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA
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Jian Xu , Wei Wang , Jian Pei , Xiaoyuan Wang , Baile Shi , Ada Wai-Chee Fu, Utility-based anonymization using local recoding, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA
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Gagan Aggarwal , Tomás Feder , Krishnaram Kenthapadi , Samir Khuller , Rina Panigrahy , Dilys Thomas , An Zhu, Achieving anonymity via clustering, Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, June 26-28, 2006, Chicago, IL, USA
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Jian Xu , Wei Wang , Jian Pei , Xiaoyuan Wang , Baile Shi , Ada Wai-Chee Fu, Utility-based anonymization for privacy preservation with less information loss, ACM SIGKDD Explorations Newsletter, v.8 n.2, p.21-30, December 2006
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Ravi Kumar , Jasmine Novak , Bo Pang , Andrew Tomkins, On anonymizing query logs via token-based hashing, Proceedings of the 16th international conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada
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Rong Ge , Martin Ester , Wen Jin , Ian Davidson, Constraint-driven clustering, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, August 12-15, 2007, San Jose, California, USA
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Bin Zhou , Yi Han , Jian Pei , Bin Jiang , Yufei Tao , Yan Jia, Continuous privacy preserving publishing of data streams, Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, March 24-26, 2009, Saint Petersburg, Russia
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Rinku Dewri , Darrell Whitley , Indrajit Ray , Indrakshi Ray, A multi-objective approach to data sharing with privacy constraints and preference based objectives, Proceedings of the 11th Annual conference on Genetic and evolutionary computation, July 08-12, 2009, Montreal, Québec, Canada
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