A Global Optimal Model for Protecting Privacy

Since Sweeney first proposed the k-anonymity algorithm to protect the security of published data, many researchers have proposed improved algorithms based on the framework of k-anonymity. However, the existing algorithms have not reached the optimal performance in anonymity. An effective anonymity algorithm should be able to solve a basic contradiction—optimal accuracy and security trade-off. To achieve the goal, this paper first develops a new quantitative criterion for the basic contradiction based on classical probability theory. Specifically, the criterion is used to measure the possibility that the individual might experience privacy disclosure and the degree of global security and global accuracy. Through this criterion, then we can derive an optimal division theory to obtain a certain global accuracy by the minimum global security loss. The experiment result shows that the performance of our new algorithm nearly reaches the optimal balance between accuracy and security.