Title :

Interpretable privacy with optimizable utility

Author(s) :

Ramon, Jan [Auteur]
Machine Learning in Information Networks [MAGNET]
Basu, Moitree [Auteur]
Machine Learning in Information Networks [MAGNET]

Conference title :

ECML/PKDD 2020 - Workshop on eXplainable Knowledge Discovery in Data mining

City :

Ghent / Virtual

Country :

Belgique

Start date of the conference :

2020-09-14

English keyword(s) :

Privacy
Explainability
Constraint optimization

HAL domain(s) :

Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Théorie de l'information [cs.IT]
Mathématiques [math]/Statistiques [math.ST]
Statistiques [stat]/Machine Learning [stat.ML]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL