Collaborative Drug Discovery: Inference-level Data Protection Perspective

• URL: http://arxiv.org/abs/2205.06506v1
• Date: Fri, 13 May 2022 08:30:50 GMT
• Title: Collaborative Drug Discovery: Inference-level Data Protection Perspective
• Authors: Balazs Pejo, Mina Remeli, Adam Arany, Mathieu Galtier, Gergely Acs
• Abstract summary: Pharmaceutical industry can better leverage its data assets to virtualize drug discovery through a collaborative machine learning platform. There are non-negligible risks stemming from the unintended leakage of participants' training data. 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.
• Score: 2.624902795082451
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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