General Information

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.