Privacy-Preserving Data Publishing
Foundations and Trends in Databases
The k-anonymity problem is hard
FCT'09 Proceedings of the 17th international conference on Fundamentals of computation theory
Efficient Anonymizations with Enhanced Utility
Transactions on Data Privacy
Parameterized complexity of k-anonymity: hardness and tractability
IWOCA'10 Proceedings of the 21st international conference on Combinatorial algorithms
On the complexity of the l-diversity problem
MFCS'11 Proceedings of the 36th international conference on Mathematical foundations of computer science
Finding all maximally-matchable edges in a bipartite graph
Theoretical Computer Science
Limiting disclosure of sensitive data in sequential releases of databases
Information Sciences: an International Journal
Secure distributed computation of anonymized views of shared databases
ACM Transactions on Database Systems (TODS)
A practical approximation algorithm for optimal k-anonymity
Data Mining and Knowledge Discovery
k-Concealment: An Alternative Model of k-Type Anonymity
Transactions on Data Privacy
Towards an automatic construction of Contextual Attribute-Value Taxonomies
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Parameterized complexity of k-anonymity: hardness and tractability
Journal of Combinatorial Optimization
Improving accuracy of classification models induced from anonymized datasets
Information Sciences: an International Journal
The effect of homogeneity on the computational complexity of combinatorial data anonymization
Data Mining and Knowledge Discovery
The l-Diversity problem: Tractability and approximability
Theoretical Computer Science
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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 [23] and Aggarwal et al. [1]. 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 [1]. 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 [1] in order to obtain an O(k)-approximation algorithm that is polynomial in both n and k.