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- W2159466162 abstract "In this paper we consider the on-line max query auditing problem: given a private association between fields in a data set, a sequence of max queries that have already been posed about the data, their corresponding answers and a new query, deny the answer if a private information is inferred or give the true answer otherwise. We give a probabilistic definition of privacy and demonstrate that max queries can be audited in a simulatable paradigm by means of a Bayesian network. Moreover, we show how our auditing approach is able to manage user prior-knowledge." @default.
- W2159466162 created "2016-06-24" @default.
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- W2159466162 date "2008-03-01" @default.
- W2159466162 modified "2023-09-26" @default.
- W2159466162 title "A Bayesian Approach for on-Line Max Auditing" @default.
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- W2159466162 doi "https://doi.org/10.1109/ares.2008.94" @default.
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