Anonymizing 1:M microdata with high utility.

Preserving privacy and utility during data publishing and data mining is essential for individuals, data providers and researchers. However, studies in this area typically assume that one individual has only one record in a dataset, which is unrealistic in many applications. Having multiple records for an individual leads to new privacy leakages. We call such a dataset a 1:M dataset. In this paper, we propose a novel privacy model called (k, l)-diversity that addresses disclosure risks in 1:M data publishing. Based on this model, we develop an efficient algorithm named 1:M-Generalization to preserve privacy and data utility, and compare it with alternative approaches. Extensive experiments on real-world data show that our approach outperforms the state-of-the-art technique, in terms of data utility and computational cost.