Securing Database by Using Collaborative Inference Detection

Database is a major component of global information infrastructure. Thus securing these databases is very important thing. Thus, we develop an inference violation detection system to protect sensitive data content. Based on Knowledge acquisition, data dependency, database schema, and semantic knowledge, we constructed a semantic inference model (SIM) that represents the possible inference channels from any attribute to the pre-assigned 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 users past query log and calculate the probability of inferring sensitive information. The query request will be denied if the inference probability exceeds the pre-specified threshold. For multiuser cases, the users may share their query answers to increase the inference probability. Therefore, we develop a model for evaluating collaborative inference based on the query sequences of collaborators and their task-sensitive collaboration levels. Experimental studies reveal that information authoritativeness, communication fidelity, and honesty in collaboration are three key factors that affect the level of achievable collaboration. In order to prevent an adversary from inferring information from a database, inference analyst must be able to detect and prevent possible inferences.