A blackboard architecture for control
Artificial Intelligence
An introduction to database systems: vol. I (4th ed.)
An introduction to database systems: vol. I (4th ed.)
Artificial Intelligence
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Proceedings from the first international workshop on Expert database systems
Security and inference in multilevel database and knowledge-base systems
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
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ACM Transactions on Database Systems (TODS)
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Communications of the ACM
Constraint Equations: Declarative Expression of Constraints With Automatic Enforcement
VLDB '84 Proceedings of the 10th International Conference on Very Large Data Bases
Auditing user queries in dynamic statistical databases
Information Sciences: an International Journal
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STM'10 Proceedings of the 6th international conference on Security and trust management
Privacy preserving via tree augmented naïve Bayesian classifier in multimedia database
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
Dynamic disclosure monitor (D2Mon): an improved query processing solution
SDM'05 Proceedings of the Second VDLB international conference on Secure Data Management
A scheme for inference problems using rough sets and entropy
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Unauthorized inferences in semistructured databases
Information Sciences: an International Journal
Medical privacy protection based on granular computing
Artificial Intelligence in Medicine
Modeling and preventing inferences from sensitive value distributions in data release
Journal of Computer Security - STM'10
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The potential for logical inference of high level information based upon lower level visible data presents an interesting and challenging threat to multilevel security. Such compromises of security are rather novel since they circumvent traditional security mechanisms and rely on a user's knowledge of the application, which is external to the security layers of the system. The potential for such inferences, and the multiple consequences of a corrective action, substantially complicate the task of classifying the data in a secure manner. Computer-based tools will be needed to assist in this process, especially when multilevel databases of substantial size and complexity are considered. Heretofore, the problems of inference and security have been amorphous and difficult to circumscribe. This paper proposes a framework for studying these inference control problems, describes a representation for relevant semantics of the application, develops criteria for safety and security of a system to prevent these problems, and describes the functionality of the proposed classification tool in terms of a scenario for its use.