k-Anonymization with Minimal Loss of Information

The technique of k -anonymization allows the releasing of databases that contain personal information while ensuring some degree of individual privacy. Anonymization is usually performed by generalizing database entries. We formally study the concept of generalization, and propose three information-...

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Vydané v:	IEEE transactions on knowledge and data engineering Ročník 21; číslo 2; s. 206 - 219
Hlavní autori:	Gionis, A., Tassa, T.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.02.2009 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Approximation Approximation algorithms approximation algorithms for NP-hard problems Computer science; control theory; systems Cryptography Data mining Data privacy Data processing. List processing. Character string processing Data security Exact sciences and technology Graphs Hospitals Information retrieval. Graph Information security Information theory Information, signal and communications theory k-anonymization Medical diagnostic imaging Memory organisation. Data processing Optimization Polynomials Privacy-preserving data mining Releasing Representations Running Software Studies Telecommunications and information theory Theoretical computing Knowledge and data engineering tools and techniques integrity Security Data mining and protection Mining methods and algorithms Hypergraph Private life Data privacy k-anonymization Polynomial approximation Anonymity Graph theory Polynomial method Approximation algorithm Information loss Polynomial time Information measure approximation algorithms for NP-hard problems Optimal solution NP hard problem Database Best approximation Optimal approximation Privacy-preserving data mining Information theory
ISSN:	1041-4347, 1558-2191
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Shrnutí:	The technique of k -anonymization allows the releasing of databases that contain personal information while ensuring some degree of individual privacy. Anonymization is usually performed by generalizing database entries. We formally study the concept of generalization, and propose three information-theoretic measures for capturing the amount of information that is lost during the anonymization process. The proposed measures are more general and more accurate than those that were proposed by Meyerson and Williams and Aggarwal et al. We study the problem of achieving k -anonymity with minimal loss of information. We prove that it is NP-hard and study polynomial approximations for the optimal solution. Our first algorithm gives an approximation guarantee of O(ln k ) for two of our measures as well as for the previously studied measures. This improves the best-known O( k )-approximation in. While the previous approximation algorithms relied on the graph representation framework, our algorithm relies on a novel hypergraph representation that enables the improvement in the approximation ratio from O( k ) to O(ln k ). As the running time of the algorithm is O(n 2k }), we also show how to adapt the algorithm in in order to obtain an O( k )-approximation algorithm that is polynomial in both n and k .
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23
ISSN:	1041-4347 1558-2191
DOI:	10.1109/TKDE.2008.129