ANGELMS: A privacy preserving data publishing framework for microdata with multiple sensitive attributes

Multi-dimension bucketization is a typical framework for preventing privacy disclosure of microdata with multiple sensitive attributes. However, it results in too much tuple suppression when the considered microdata have more than 3 sensitive attributes. Besides, it does not generalize quasi-identifiers, which make the anonymized data easy to suffer from linking attack. To overcome these drawbacks, we propose an improved bucketization framework, named ANGELMS. ANGELMS first vertically partitions sensitive attributes into several independent tables, and then bucketizes them according to l-diversity principle and generalizes quasi-identifiers according to k-anonymity principle. In addition, we proposed an MSB-KACA algorithm for the k-anonymizing process of our ANGELMS framework. Experiments show that the proposed framework can generate anonymized tables with less information loss and suppress ratio than simple multi-dimension bucketization do.