Privacy Inference on Knowledge Graphs: Hardness and Approximation

The rapid information propagation facilitates our work and life without precedent in history, but it has tremendously exaggerated the risk and consequences of privacy invasion. Today's attackers are becoming more and more powerful in gathering personal information from many sources and mining these data to further uncover users' privacy. A great number of previous works have shown that, with adequate background knowledge, attackers are even able to infer sensitive information that is not revealed to anyone malicious before. In this paper, we model the attacker's knowledge using a knowledge graph and formally define the privacy inference problem. We show its #P-hardness and design an approximation algorithm to perform privacy inference in an iterative fashion, which also reflects real-life network evolution. The simulations on two data sets demonstrate the feasibility and efficacy of privacy inference using knowledge graphs.