Privacy Preserving Enhancing Model for Multiple-sensitive Attributes

Recently, there is a demand of publishing sensitive data about individuals for many research reasons. However, this will compromise the privacy of the individuals. Therefore, anonymity concept needed to be applied on the data before publishing it. The problem is that most of anonymity models will have information leak when dealing with multiple sensitive attributes. In this paper, a privacy preserving enhancing model for multiple sensitive attributes is presented based on the Anatomy. This model can be achieved by clustering the data based on the Anatomy principles on sensitive attributes together using Single Sensitive Bucketization SSB. M-Buckets algorithm is presented and compared with Anatomy algorithm. Results show better privacy preserving for the proposed algorithm.