Protection of Data Base Security via Collaborative Inference Detection

Malicious users can exploit the correlation among data to infer sensitive information from a series of seemingly innocuous data accesses. Thus, we will develop an inference violation detection system to protect sensitive data content. Based on data dependency, database schema, and semantic knowledge, we have to construct a semantic inference model (SIM) that represents the possible inference channels from any attribute to the preassigned sensitive attributes. The SIM is then instantiated to a semantic inference graph (SIG) for query-time inference violation detection. For a single user case, when a user poses a query, the detection system will examine his/her past query log and calculate the probability of inferring sensitive information. The query request will be denied if the inference probability exceeds the prespecified threshold. For multiuser cases, the users may share their query answers to increase the inference probability. Therefore, we will develop a model for evaluating collaborative inference based on the query sequences of collaborators and their task-sensitive collaboration levels.