A Personalized Privacy Anonymous Method Based on Inverse Clustering
Jing Yang · Dianzi xuebao · 2012
For achieving the different privacy preservation requirements of each individual,this paper presents a personalized extension l-diversity privacy anonymous model orienting individuals.This model proposes an extension l-diversity principle based on the traditional l-diversity,and realizes the requirement of personalized protection of relationship between individual and sensitive value by setting up guarding attributes on sensitive attributes.In the meantime,this paper also proposes a personalized extension l-diversity inverse clustering algorithm(PELI-clustering) to implement the privacy anonymous model presented in this paper.The experiments show that the proposed algorithm in this paper not only meets the requirements of personalized service,but also produces similar information loss to the traditional clustering-based l-diversity algorithm with less time cost,which achieves more effective privacy preservation.