Revising Belief without Revealing Secrets

Biskup, Joachim; Tadros, Cornelia

doi:10.1007/978-3-642-28472-4_4

Cited by 12 publications

(5 citation statements)

References 14 publications

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“…The last two AGM axioms are in fact not characteristic of the revision problem and one may argue that, by means of the two axioms, the AGM approach chooses as much an uncertainty framework that is not probabilistic by means of such two axioms as it provides core axioms for belief revision (the first six). Note that the converse belief revision problem discussed here is different from the converse problem of iterated belief revision addressed in [8] and the converse belief revision in multi-agent systems [5]. Here, the multiple revision scenarios do not correspond to a sequence of revision actions, where the iteration may cause successive changes of an agent's belief structure.…”

Section: Introductionmentioning

confidence: 94%

Rational Partial Choice Functions and Their Application to Belief Revision

Liu

Dubois

2015

Knowledge Science, Engineering and Management

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Abstract. Necessary and sufficient conditions for choice functions to be rational have been intensively studied in the past. However, in these attempts, a choice function is completely specified. That is, given any subset of options, called an issue, the best option over that issue is always known, whilst in real-world scenarios, it is very often that only a few choices are known instead of all. In this paper, we study partial choice functions and investigate necessary and sufficient rationality conditions for situations where only a few choices are known. We prove that our necessary and sufficient condition for partial choice functions boils down to the necessary and sufficient conditions for complete choice functions proposed in the literature. Choice functions have been instrumental in belief revision theory. That is, in most approaches to belief revision, the problem studied can simply be described as the choice of possible worlds compatible with the input information, given an agent's prior belief state. The main effort has been to devise strategies in order to infer the agents revised belief state. Our study considers the converse problem: given a collection of input information items and their corresponding revision results (as provided by an agent), does there exist a rational revision operation used by the agent and a consistent belief state that may explain the observed results?

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Section: Introductionmentioning

confidence: 94%

Rational Partial Choice Functions and Their Application to Belief Revision

Liu

Dubois

2015

Knowledge Science, Engineering and Management

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“…For semantic web applications, the authors of (Kagal et al, 2003) have proposed policy languages to represent obligation and delegation policies based on access control approach. Biskup et al in (Biskup and Weibert, 2008;Biskup and Tadros, 2012) studied secrecy in incomplete databases using controlled query evaluation (CQE). Since description logics (DLs) underlie web ontology languages (OWLs), recently researchers have shown an interest in studying secrecypreserving reasoning in DL knowledge bases (KBs).…”

Section: Introductionmentioning

confidence: 99%

Secrecy-Preserving Query Answering in ELH Knowledge Bases

Sivaprakasam

Slutzki

2016

Proceedings of the 8th International Conference on Agents and Artificial Intelligence

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In this paper we study Secrecy-Preserving Query Answering problem under Open World Assumption (OWA) for ELH Knowledge Bases (KBs). We employ two tableau procedures designed to compute some consequences of ABox (A) and TBox (T) denoted by A * and T * respectively. A secrecy set of a querying agent is subset S of A * ∪ T * which the agent is not allowed to access. An envelope is a superset of the secrecy set which provides logical protection to the secrecy set against the reasoning of the querying agent. Once envelopes are computed, they are used to efficiently answer assertional and GCI queries without compromising the secret information in S. Answering GCI queries while preserving secrecy has not been studied in the current literature. When the querying agent asks a query q, the reasoner answers "Yes" if KB | = q and q does not belong to the envelopes; otherwise, the reasoner answers "Unknown". Being able to answer "Unknown" plays a key role in protecting secrecy under OWA. Since we are not computing all the consequences of the KB, answers to the queries based on just A * and T * could be erroneous. To fix this problem, we further augment our algorithms to make the query answering procedure foolproof.

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“…One approach to secrecy in incomplete database was presented in Biskup and Weibert (2008); Biskup et al (2010); Biskup and Tadros (2012) in the form of controlled query evaluation (CQE). The idea behind CQE is that rather than providing strict access control to data, the CQE approach enforces secrecy by checking (at run time) whether from a truthful answer to a query a user can deduce secret information.…”

Section: Secrecy-preserving Query Answering Problemmentioning

confidence: 99%

“…For semantic web applications, the authors of Kagal et al (2003) have proposed policy languages to represent obligation and delegation policies based on access control approach. Biskup et al in Biskup and Weibert (2008); Biskup and Tadros (2012) studied secrecy in incomplete databases using controlled query evaluation (CQE). Since description logics (DLs) underlie web ontology languages (OWLs), recently researchers have shown an interest in studying secrecy-preserving reasoning in DL knowledge bases (KBs).…”

Section: Introductionmentioning

confidence: 99%

Secrecy-preserving reasoning in simple description logic knowledge bases

Sivaprakasam

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Revising Belief without Revealing Secrets

Cited by 12 publications

References 14 publications

Rational Partial Choice Functions and Their Application to Belief Revision

Rational Partial Choice Functions and Their Application to Belief Revision

Secrecy-Preserving Query Answering in ELH Knowledge Bases

Secrecy-preserving reasoning in simple description logic knowledge bases

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