Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Software engineering mathematics
Software engineering mathematics
Toward a tool to detect and eliminate inference problems in the design of multilevel databases
Results of the Sixth Working Conference of IFIP Working Group 11.3 on Database Security on Database security, VI : status and prospects: status and prospects
AERIE: an inference modeling and detection approach for databases
Results of the Sixth Working Conference of IFIP Working Group 11.3 on Database Security on Database security, VI : status and prospects: status and prospects
Inference through secondary path analysis
Results of the Sixth Working Conference of IFIP Working Group 11.3 on Database Security on Database security, VI : status and prospects: status and prospects
Knowledge Discovery in Databases
Knowledge Discovery in Databases
The Use of Conceptual Structures for Handling the Inference Problem
Results of the IFIP WG 11.3 Workshop on Database Security V: Status and Prospects
Aggregation in Relational Databases: Controlled Disclosure of Sensitive Information
ESORICS '94 Proceedings of the Third European Symposium on Research in Computer Security
Secure Databases: Constraints, Inference Channels, and Monitoring Disclosures
IEEE Transactions on Knowledge and Data Engineering
The inference problem: a survey
ACM SIGKDD Explorations Newsletter
The inference problem and updates in relational databases
Das'01 Proceedings of the fifteenth annual working conference on Database and application security
IEEE Transactions on Knowledge and Data Engineering
Privacy-preserving data integration and sharing
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Sanitization models and their limitations
NSPW '06 Proceedings of the 2006 workshop on New security paradigms
Auditing Inference Based Disclosures in Dynamic Databases
SDM '08 Proceedings of the 5th VLDB workshop on Secure Data Management
Query rewriting for detection of privacy violation through inferencing
Proceedings of the 2006 International Conference on Privacy, Security and Trust: Bridge the Gap Between PST Technologies and Business Services
Evaluating privacy threats in released database views by symmetric indistinguishability
Journal of Computer Security - Selected papers from the Third and Fourth Secure Data Management (SDM) workshops
Suppressing microdata to prevent classification based inference
The VLDB Journal — The International Journal on Very Large Data Bases
A systematic literature review of inference strategies
International Journal of Information and Computer Security
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
A model-theoretic approach to data anonymity and inference control
Proceedings of the second ACM conference on Data and Application Security and Privacy
Indistinguishability: the other aspect of privacy
SDM'06 Proceedings of the Third VLDB international conference on Secure Data Management
Unauthorized inferences in semistructured databases
Information Sciences: an International Journal
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Database systems that contain information of varying degrees of sensitivity pose the threat that some of the Low data may infer High data. This study derives conditions sufficient to identify such inference threats. First, it is reasoned that a database can only control material implications, as specified in formal logic systems. These material implications are found using Knowledge Discovery techniques. Material implications allow reasoning about outside knowledge, and provide the first assurance that outside knowledge does not assist in circumventing the inference controls. Database queries specify the properties of sets of data and are compared to help determine inferences. These queries are grouped into equivalence classes based upon their inference characteristics. A unique graph based model is developed for the equivalence classes that 1) makes such comparisons easy, and 2) allows implementation of an algorithm capable of finding those material implication rules where High data is inferred from Low data. This is the first method that offers assurance and sufficiency arguments that the mechanism is at least strong enough to protect the High data in the database from inference attacks that require Low data.