Hard problems for simple default logics
Artificial Intelligence - Special issue on knowledge representation
Default theories of Poole-type and a method for constructing cumulative versions of default logic
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
ACM Transactions on Database Systems (TODS)
Formal query languages for secure relational databases
ACM Transactions on Database Systems (TODS)
Foundations of Secure Deductive Databases
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Default Logic as a Query Language
IEEE Transactions on Knowledge and Data Engineering
Equilibria in heterogeneous nonmonotonic multi-context systems
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A new perspective on stable models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Using ASP for knowledge management with user authorization
Data & Knowledge Engineering
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
The complexity of propositional default logics
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
The DMCS solver for distributed nonmonotonic multi-context systems
JELIA'10 Proceedings of the 12th European conference on Logics in artificial intelligence
The MCS-IE system for explaining inconsistency in multi-context systems
JELIA'10 Proceedings of the 12th European conference on Logics in artificial intelligence
The relationship between reasoning about privacy and default logics
LPAR'05 Proceedings of the 12th international conference on Logic for Programming, Artificial Intelligence, and Reasoning
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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.