Privacy Preserving Approaches For Relational Multiple Sensitive Attributes

With the rapid development of data mining technology and network technology, How to solve information sharing and privacy protection in the process of data publishing has become an important research. Data releasing is often involved relevant sensitive attributes in real applications. Directly applying the existing sensitive attribute privacy protection methods to multiple sensitive attributes privacy protection can divulge the privacy data. This paper proposes a method of protecting privacy which aims at data containing relevant multi-sensitive attribute.This paper proposes the concepts about independent relevant multi-sensitive attributes and joint relevant multi-sensitive attributes based on attribute selection .The records will be protected after classified by their nature, and discusses the method of privacy protection. The thought of lossy join for protecting privacy data is inherited, and the records are clustered to guarantee the sensitive rank division in tables. The records divided were grouped according to frequency of comparative strategy. And a grouping algorithm which is (k, p)-sensitive relevant multi-sensitive attribute method is proposed based on clustering. The experimental results indicated that this algorithm could prevent the privacy revelation, and strengthen security of data published. The method produces greater additional information loss, so this paper continues with improvement for above method. Improved (k, p)-sensitive firstly adopts the method of circular cluster to deal with the remaining records, increase group and reduce the second adding.The experimental results of (k, p)-sensitive indicated that this algorithm could prevent the privacy revelation, and strengthen security of data published. The results of improved (k, p)-sensitive shows that the additional information loss is further reduced by loss joint and strengthen data sharing on the basis of effectively preventing disclosure .