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.
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Canfora, G., Cavallo, B. (2008). Reasoning under Uncertainty in On-Line Auditing. In: Domingo-Ferrer, J., Saygın, Y. (eds) Privacy in Statistical Databases. PSD 2008. Lecture Notes in Computer Science, vol 5262. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87471-3_22
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DOI: https://doi.org/10.1007/978-3-540-87471-3_22
Publisher Name: Springer, Berlin, Heidelberg
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