Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Security and inference in multilevel database and knowledge-base systems
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Fundamentals of database systems
Fundamentals of database systems
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
Proceedings on Conceptual Graphs for Knowledge Representation
ICCS '93 Proceedings on Conceptual Graphs for Knowledge Representation
Proceedings of the Second International Conference on Conceptual Structures: Current Practices
ICCS '94 Proceedings of the Second International Conference on Conceptual Structures: Current Practices
AERIE: Database Inference Modeling and Detection Using Conceptual Graphs
Proceedings of the 7th Annual Workshop on Conceptual Structures: Theory and Implementation
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
Layered Knowledge Chunks for Database Inference
Proceedings of the IFIP WG11.3 Working Conference on Database Security VII
Detection and Elimination of Inference Channels in Multilevel Relational Database Systems
SP '93 Proceedings of the 1993 IEEE Symposium on Security and Privacy
The inference problem: a survey
ACM SIGKDD Explorations Newsletter
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
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
Auditing user queries in dynamic statistical databases
Information Sciences: an International Journal
Semantics-aware security policy specification for the semantic web data
International Journal of Information and Computer Security
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
Efficient inference control for open relational queries
DBSec'10 Proceedings of the 24th annual IFIP WG 11.3 working 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
Database security protection via inference detection
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Indistinguishability: the other aspect of privacy
SDM'06 Proceedings of the Third VLDB international conference on Secure Data Management
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The database inference problem is a well-known problem in database security and information system security in general. In order to prevent an adversary from inferring classified information from combinations of unclassified information, a database inference analyst must be able to detect and prevent possible inferences. Detecting database inference problems at database design time provides great power in reducing problems over the lifetime of a database. We have developed and constructed a system called Wizard to analyze databases for their inference problems. The system takes as input a database schema, its constituent instances (if available) and additional human-supplied domain information, and provides a set of associations between entities and/or activities that can be grouped by their potential severity of inference vulnerability. A knowledge acquisition process called microanalysis permits semantic knowledge of a database to be incorporated into the analysis using conceptual graphs. These graphs are then analyzed with respect to inference-relevant domains we call facets using tools we have developed. We can determine inference problems within single facets as well as some inference problems between two or more facets. The architecture of the system is meant to be general so that further refinements of inference information subdomains can be easily incorporated into the system.