Utility-based k-anonymization

k-anonymity is a well-researched mechanism for protecting private information released in Web. It requires that each tuple of a public released table must be indistinguishable from at least other k - 1 tuples. Subject to this constraint, how to release data as useful as possible is challenge. Most previous works try to develop flexible anonymization method to reduce information loss, however, utility of released data is ignored. This paper studies utility-based k-anonymization. We first analyze deficiencies of previous global receding model and the state-of-the-art local receding model from utility view. Best of our knowledge, this is the first paper to evaluate the two models from such a view. Effectively combing both global and local recoding, we then propose a hybrid algorithm for utility based k-anonymization. The algorithm greedily partitions original table into non-overlapping sub-tables in global recoding phase, and then employ local recoding to go on divide each sub-table into smaller ones if possible. Experiments on famous adult data set show the utility and also information loss advantage of our algorithm towards the advanced algorithms proposed in recent related literatures.