Personalized Privacy Preserving Approaches for Multiple Sensitive Attributes in Data Publishing

Abstract

n recent years, more and more research for multiple sensitive attributes of personal privacy preserving technology, and put forward a variety of methods, but most of the multiple sensitive attributes privacy protection method have not achieved autonomy of personal privacy, personalized privacy preserving. This paper proposed a personalized privacy preserving model for multiple sensitive attributes based on multi-sensitive bucketization, the model with personalized (, l) - anonymity model of multi dimension bucket grouping technology to realize personalized preserving. The general multi-sensitive personal-(,l)(GMSP) and the maximum selectivity personal first (MSPF) are proposed, which is based on the as the parameter. Through a lot of experiments on real data sets, the results show that the model can reduce the leakage of sensitive data and enhance the data publishing.