A method to translate privacy requirements ...
Type de document :
Autre communication scientifique (congrès sans actes - poster - séminaire...)
Titre :
A method to translate privacy requirements into a configuration for privacy-preserving machine learning applied to multi-centric studies
Auteur(s) :
Basu, Moitree [Auteur]
Machine Learning in Information Networks [MAGNET]
Ramon, Jan [Auteur]
Machine Learning in Information Networks [MAGNET]
Machine Learning in Information Networks [MAGNET]
Ramon, Jan [Auteur]
![refId](/themes/Mirage2//images/idref.png)
Machine Learning in Information Networks [MAGNET]
Titre de la manifestation scientifique :
French health data hub's AI4Health Winter School
Ville :
virtual
Pays :
France
Date de début de la manifestation scientifique :
2021-01-04
Discipline(s) HAL :
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Théorie de l'information [cs.IT]
Mathématiques [math]/Statistiques [math.ST]
Physique [physics]/Physique [physics]/Analyse de données, Statistiques et Probabilités [physics.data-an]
Statistiques [stat]/Machine Learning [stat.ML]
Informatique [cs]/Théorie de l'information [cs.IT]
Mathématiques [math]/Statistiques [math.ST]
Physique [physics]/Physique [physics]/Analyse de données, Statistiques et Probabilités [physics.data-an]
Statistiques [stat]/Machine Learning [stat.ML]
Résumé en anglais : [en]
Recently, due to the potential of machine learning approaches and the increased awareness of privacy risks, there is an increased interest in privacy-preserving machine learning. However, real-world medical applications ...
Lire la suite >Recently, due to the potential of machine learning approaches and the increased awareness of privacy risks, there is an increased interest in privacy-preserving machine learning. However, real-world medical applications are often complex. Our research, therefore, focuses on two objectives: we want to develop algorithms that make privacy-preserving machine learning more interpretable for non-experts and which automatically optimize the parameters and strategy of a machine learning solution to respect privacy requirements while optimizing utility, i.e., maximizing precision and minimizing cost. At its core, our methodology is based on a constraint programming approach. It is known that non-experts can relatively easily learn to express requirements in the form of constraints. At the same time, this approach allows us to use a wide range of publicly available constraint program solvers.Lire moins >
Lire la suite >Recently, due to the potential of machine learning approaches and the increased awareness of privacy risks, there is an increased interest in privacy-preserving machine learning. However, real-world medical applications are often complex. Our research, therefore, focuses on two objectives: we want to develop algorithms that make privacy-preserving machine learning more interpretable for non-experts and which automatically optimize the parameters and strategy of a machine learning solution to respect privacy requirements while optimizing utility, i.e., maximizing precision and minimizing cost. At its core, our methodology is based on a constraint programming approach. It is known that non-experts can relatively easily learn to express requirements in the form of constraints. At the same time, this approach allows us to use a wide range of publicly available constraint program solvers.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
Fichiers
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- cp_ai4health.pdf
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