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On the complexity of optimal K-anonymity
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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
Authors
Adam Meyerson  University of California, Los Angeles, CA
Ryan Williams  Carnegie Mellon University, Pittsburgh, PA
Sponsors
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGMOD: ACM Special Interest Group on Management of Data
SIGART: ACM Special Interest Group on Artificial Intelligence
Publisher
ACM  New York, NY, USA
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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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R. Agrawal, J. Kiernan, R. Srikant, and Y. Xu. Hippocratic databases. In Proc. of the 28th International Conference on Very Large Databases, 143--154, 2002.
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D. S. Johnson. Approximation algorithms for combinatorial problems. Journal of Computer and System Sciences 9:256--278, 1974.
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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
Collaborative Colleagues:
Adam Meyerson: colleagues
Ryan Williams: colleagues