Constraints, Inference Channels and Secure Databases

Abstract

This paper investigates the problem of con dentiality viola tions via illegal data inferences that occur when arithmetic constraints are combined with non con dential numeric data to infer con dential in formation The database is represented as a point in an n k dimensional constraint space where n is the number of numerical data items stored in the database extensional database and k is the number of derivable attributes intensional database Database constraints over both exten sional and intensional databases form an n k dimensional constraint object A query answer over a data item x is an interval I of values along the x axis of the database such that I is correct i e the actual data value is within I and safe i e users cannot infer which point within I is the actual data value The security requirements are expressed by the accuracy with which users are allowed to disclose data items More speci cally we develop two classi cation methods volume based clas si cation where the entire volume of the disclosed constraint object that contains the data item is considered and interval based classi cation where the length of the interval that contains the data item is considered We develop correct and safe inference algorithms for both cases Contact Author Csilla Farkas Mail Stop A George Mason University Fairfax VA Telephone Fax Internet cfarkas gmu edu

DOI: 10.1007/3-540-45349-0_9

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Cite this paper

@inproceedings{Brodsky2000ConstraintsIC, title={Constraints, Inference Channels and Secure Databases}, author={Alexander Brodsky and Csilla Farkas and Duminda Wijesekera and Xiaoyang Sean Wang}, booktitle={CP}, year={2000} }