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A method to translate privacy requirements into a configuration for privacy-preserving machine learning applied to multi-centric studies

Abstract

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.
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Dates and versions

hal-03964653 , version 1 (31-01-2023)

Identifiers

  • HAL Id : hal-03964653 , version 1

Cite

Moitree Basu, Jan Ramon. A method to translate privacy requirements into a configuration for privacy-preserving machine learning applied to multi-centric studies. French health data hub's AI4Health Winter School, Jan 2021, virtual, France. ⟨hal-03964653⟩
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