STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Generalizing data to provide anonymity when disclosing information (abstract)
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Journal of Computer and System Sciences - Special issue on PODS 2000
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
ICDT'05 Proceedings of the 10th international conference on Database Theory
Toward privacy in public databases
TCC'05 Proceedings of the Second international conference on Theory of Cryptography
Anonymizing binary and small tables is hard to approximate
Journal of Combinatorial Optimization
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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 two information-theoretic measures for capturing the amount of information that is lost during the anonymization process. Those measures are more general and more accurate than those proposed in [19] and [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) - an improvement over the best-known O(k)-approximation of [1]. As the running time of the algorithm is O(n2k), we also show how to adapt the algorithm of [1] in order to obtain an O(k)-approximation algorithm that is polynomial in both n and k.