A generalization model for multi-record privacy preservation

Abstract

Privacy preservation becomes a more and more serious problem in data publication, which has drawn dramatic attention in research and development. Recently, several privacy preservation models and algorithms have been proposed for publishing data. However, most of the previous methods suffer from more than one drawback as follows: (i) Could not be used on multi-record datasets. (ii) Only guarantee one-way generalization. (iii) User privacy preferences are ignored. In order to satisfy higher privacy requirements and make it suitable for multi-record publishing datasets, a bidirectional personalized generalization (BP-generalization) model is proposed as a new solution in this paper. The rational is to focus anonymous objects on both relational and set-valued information. First, we merge tuples with the same attribute values in multi-record datasets to ensure the validity of quasi-identifier anonymity. Second, by enforcing l-diversity on equivalence groups and k-anonymity on fingerprint buckets respectively, privacy preservation model may resist bi-directional chain attack. Finally, a new hierarchical generalization strategy is also proposed for personal privacy preservation of sensitive attributes, then different generalization rules can be adopted for different levels of sensitive values. Extensive experimental results on two datasets show that the performance of our method is better than state-of-art techniques in terms of efficiency and information loss.