Privacy preservation using multi-context systems and default logic

Jürgen Dix*, Wolfgang Faber, V. S. Subrahmanian

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter


Preserving the privacy of sensitive data is one of the major challenges the information society has to face. Traditional approaches focused on infrastructures for identifying data which is to be kept private and for managing access rights to these data. However, although these efforts are useful, they do not address an important aspect: While the sensitive data itself can be protected nicely using these mechanisms, related data, which is deemed insensitive per se, may be used to infer sensitive data. This inference can be achieved by combining insensitive data or by exploiting specific background knowledge of the domain of discourse. In this paper, we present a general formalization of this problem and two particular instantiations of it. The first supports query answering by means of multi-context systems and hybrid knowledge bases, while the second allows for query answering by using default logic.

Original languageEnglish (US)
Title of host publicationCorrect Reasoning
Subtitle of host publicationEssays on Logic-Based AI in Honor of Vladimir Lifschitz
EditorsErdem Esra, Lee Joohyung, Lierler Yuliya, Pearce David
Number of pages16
StatePublished - 2012
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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