Reasoning under Uncertainty in On-Line Auditing

Authors:
Gerardo Canfora;Bice Cavallo
Affiliations:
Department of Engineering, University of Sannio, Benevento, Italy;Department of Engineering, University of Sannio, Benevento, Italy
Venue:
PSD '08 Proceedings of the UNESCO Chair in data privacy international conference on Privacy in Statistical Databases
Year:
2008

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Abstract

We propose a Bayesian approach to reasoning under uncertainty in on-line auditing of Statistical Databases. A Bayesian network addresses disclosures based on probabilistic inferences that can be drawn from released data. In particular, we deal with on-line max and min auditing. Moreover, we show how our model is able to deal with the implicit delivery of information that derives from denying the answer to a query and to manage user prior-knowledge.