Collaborative Drug Discovery: Inference-level Data Protection Perspective

• Published in: arXiv.org
• Main Authors: Pejo, Balazs, Remeli, Mina, Arany, Adam, Galtier, Mathieu, Acs, Gergely
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 09.06.2022
• Subjects: Collaboration; Inference; Machine learning; Privacy; Risk assessment; Taxonomy

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.