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(n2k), 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.
|Number of pages||14|
|Journal||IEEE Transactions on Knowledge and Data Engineering|
|State||Published - Feb 2009|
- Approximation algorithms for NP-hard problems
- Privacy-preserving data mining