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Interpretable privacy with optimizable utility

Jan Ramon 1 Moitree Basu 1
1 MAGNET - Machine Learning in Information Networks
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : In this position paper, we discuss the problem of specifying privacy requirements for machine learning based systems, in an inter-pretable yet operational way. Explaining privacy-improving technology is a challenging problem, especially when the goal is to construct a system which at the same time is interpretable and has a high performance. In order to address this challenge, we propose to specify privacy requirements as constraints, leaving several options for the concrete implementation of the system open, followed by a constraint optimization approach to achieve an efficient implementation also, next to the interpretable privacy guarantees.
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Submitted on : Monday, September 28, 2020 - 2:04:13 PM
Last modification on : Thursday, February 11, 2021 - 1:33:31 PM
Long-term archiving on: : Tuesday, December 29, 2020 - 6:06:18 PM


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  • HAL Id : hal-02950994, version 1


Jan Ramon, Moitree Basu. Interpretable privacy with optimizable utility. ECML/PKDD 2020 - Workshop on eXplainable Knowledge Discovery in Data mining, Sep 2020, Ghent / Virtual, Belgium. ⟨hal-02950994⟩



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