Collaborative Drug Discovery: Inference-level Data Protection Perspective

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 pl...

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Bibliographic Details
Published inarXiv.org
Main Authors Pejo, Balazs, Remeli, Mina, Arany, Adam, Galtier, Mathieu, Acs, Gergely
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 09.06.2022
Subjects
Collaboration
Inference
Machine learning
Privacy
Risk assessment
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Summary: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.
ISSN:2331-8422