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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 (CRIStAL) - 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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Contributor : Jan Ramon <>
Submitted on : Monday, September 28, 2020 - 2:04:13 PM
Last modification on : Tuesday, September 29, 2020 - 12:24:14 PM


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  • HAL Id : hal-02950994, version 1


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



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