Collaborative Drug Discovery: Inference-level Data Protection Perspective

Authors: Balazs Pejo, Mina Remeli, Adam Arany, Mathieu Galtier, Gergely Acs
Published: 2022-05-13 | Updated: 2022-06-09

Source: https://arxiv.org/abs/2205.06506

PDF: https://arxiv.org/pdf/2205.06506

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リスク分析手法 プライバシー評価 セキュアアグリゲーション

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Abstract

Pharmaceutical industry can better leverage its data assets to virtualize drug discovery through a collaborative machine learning platform. On the other hand, there are non-negligible risks stemming from the unintended leakage of participants’ training data, hence, it is essential for such a platform to be secure and privacy-preserving. This paper describes a privacy risk assessment for collaborative modeling in the preclinical phase of drug discovery to accelerate the selection of promising drug candidates. After a short taxonomy of state-of-the-art inference attacks we adopt and customize several to the underlying scenario. Finally we describe and experiments with a handful of relevant privacy protection techniques to mitigate such attacks.