Detecting Inference Channels in Private Multimedia Data via Social Networks
Proceedings of the 23rd Annual IFIP WG 11.3 Working Conference on Data and Applications Security XXIII
History-dependent inference control of queries by dynamic policy adaption
DBSec'11 Proceedings of the 25th annual IFIP WG 11.3 conference on Data and applications security and privacy
Privacy preserving via tree augmented naïve Bayesian classifier in multimedia database
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
A trust-and-risk aware RBAC framework: tackling insider threat
Proceedings of the 17th ACM symposium on Access Control Models and Technologies
An information theoretic framework for web inference detection
Proceedings of the 5th ACM workshop on Security and artificial intelligence
Dynamic policy adaptation for inference control of queries to a propositional information system
Journal of Computer Security - DBSec 2011
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Malicious users can exploit the correlation among data to infer sensitive information from a series of seemingly innocuous data accesses. Thus, we develop an inference violation detection system to protect sensitive data content. Based on data dependency, database schema and semantic knowledge, we con-structed a semantic inference model (SIM) that represents the possible inference channels from any at-tribute 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 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 pre-specified threshold. For multi-user cases, the users may share their query answers to increase the inference prob-ability. Therefore, we develop a model to evaluate 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. An example is given to illustrate the use of the proposed technique to prevent multiple collaborative users from deriving sensitive information via inference.