ACM Computing Surveys (CSUR)
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Convex Optimization
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Achieving anonymity via clustering
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Iterative rounding 2-approximation algorithms for minimum-cost vertex connectivity problems
Journal of Computer and System Sciences - Special issue on FOCS 2001
Privacy-Preserving Data Mining: Models and Algorithms
Privacy-Preserving Data Mining: Models and Algorithms
Modular community detection in networks
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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Motivated by the practical needs in privacy-preserving data publishing, we study the problem of r-anonymized clustering. The problem is to minimize the total cost between objects and cluster-centers subject to a constraint that each cluster contains a minimum number of objects. To address the inherent computational difficulty, we exploit linear program relaxation with a specialized iterative rounding strategy to find high quality solutions in an efficient manner. We conduct a series of experiments to evaluate the performance of the methods, and demonstrate its application in privacy preserving disease mapping.