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.

翻译：制药业可以通过合作机器学习平台更好地利用其数据资产,使毒品发现虚拟化。另一方面,由于参与者培训数据无意泄漏,存在不可忽略的风险,因此,这种平台必须安全和保护隐私。本文描述了在药物发现前临床阶段合作建模的隐私风险评估,以加快选择有前途的药物候选者。在对最先进的推断攻击进行简短分类后,我们采用并定制了基本情景中的几种。最后,我们用少数相关的隐私保护技术描述和实验,以缓解这类攻击。