Toward inference attacks for k-anonymity

Current research still cannot effectively prevent an inference attacker from inferring privacy information for k-anonymous data sets. To solve the issue, we must first study all kinds of aggressive reasoning behaviors and process for the attacker thoroughly. Our work focuses on describing comprehensively the inference attack and analyzing their privacy disclosures for k-anonymous data sets. In this paper, we build up a privacy inference graph based on attack graph theory, which is an extension of attack graph. The privacy inference graph describes comprehensively the inference attack in k-anonymous databases by considering attacker background knowledge and external factors. In the privacy inference graph, we introduce a concept of valid inference path to analyze the privacy disclosures in face of inference attack. According to both above, we design an algorithm to compute the n-valid inference paths. These paths can deduce some privacy information resulting in privacy disclosure. Moreover, we study the optimal privacy strategies to resist inference attack by key attribute sets and valid inference paths in the attack graph. An approximate algorithm is designed to obtain the approximate optimal privacy strategy set. At last, we prove the correctness in theory and analyze the performance of the approximate algorithm and their time complexity.