Unauthorized inferences in semistructured databases

  • Authors:
  • Csilla Farkas;Alexander Brodsky;Sushil Jajodia

  • Affiliations:
  • Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, United States;Center for Secure Information Systems, Department of Information and Software Engineering, George Mason University, Fairfax, VA 22030, United States;Center for Secure Information Systems, Department of Information and Software Engineering, George Mason University, Fairfax, VA 22030, United States

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2006

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Abstract

In this paper we study the problem of providing controlled access to confidential data stored in semistructured databases. More specifically, we focus on privacy violations via data inferences that occur when domain knowledge is combined with non-private data. We propose a formal model, called Privacy Information Flow Model, to represent the information flow and the privacy requirements. These privacy requirements are enforced by the Privacy Mediator. Privacy Mediator guarantees that users are not be able to logically entail information that violates the privacy requirements. We present an inference algorithm that is sound and complete. The inference algorithm is developed for a tree-like, semistructured data model, selection-projection queries, and domain knowledge, represented as Horn-clause constraints.