An Approach to Achieving K-partition for Preserving Privacy by Using Multi-constraint Anonymous Parameter Based on Rough Sets

We propose an approach to achieving different K-partition for preserving privacy by using the multiconstraint anonymous parameter design method based on the attribute significance of rough set, in order to reduce the imbalance phenomenon between the privacy protection and data availability caused by adopting the same anonymous intensity. In this approach, taking into account the significance of quasi-identifier attributes, we carry out the dimension division automatically and obtain multiconstraint anonymous parameters. After that an anonymous algorithm is executed on the separate partition. Experimental results show that the proposed method can obtain a better balance between the privacy protection degree and data availability.