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Volume 15 Issue 2

Collaborative Drug Discovery: Inference-level Data Protection Perspective

Balazs Pejo^(a),(*), Mina Remeli^(b), Adam Arany^(c), Mathieu Galtier^(d), Gergely Acs^(a)

Transactions on Data Privacy 15:2 (2022) 87 - 107

Abstract, PDF

(a) CrySyS Lab, BME, Hungary.

(b) University of Cambridge, United Kingdom. (Work was done while at CrySyS Lab).

(c) Stadius,KUL, Belgium.

(d) Owkin, France

e-mail:pejo @crysys.hu; mincsek @gmail.com; adam.arany @kuleuven.be; mathieu.galtier @owkin.com; acs @crysys.hu

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.

^* Corresponding author.