Explaining Recurrent Machine Learning Models: Integral Privacy Revisited
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Abstract We have recently introduced a privacy model for statistical and machine learning models called integral privacy. A model extracted from a database or, in general, the output of a function satisfies integral privacy when the number of generators of this model is sufficiently large and diverse. In this paper we show how the maximal c-consensus meets problem can be used to study the databases that generate an integrally private solution. We also introduce a definition of integral privacy based on minimal sets in terms of this maximal c-consensus meets problem.
2020 ◽
Vol 19
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pp. 207-233
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2020 ◽
Vol 2
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pp. 3-6
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2018 ◽
Vol 13
(1)
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pp. 21
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2021 ◽
2019 ◽
Vol 7
(6)
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pp. 985-990
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2020 ◽
Vol 8
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pp. 6974-6983
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2020 ◽