论文标题
协作药物发现：推理级别的数据保护视角
Collaborative Drug Discovery: Inference-level Data Protection Perspective
论文作者
论文摘要
制药行业可以更好地利用其数据资产来通过协作机器学习平台虚拟化药物发现。另一方面，由于参与者的培训数据的意外泄漏，存在不可忽略的风险，因此，对于这样一个平台，必须安全和隐私权。本文介绍了在药物发现的临床前阶段进行协作建模的隐私风险评估，以加快有前途的候选药物的选择。经过最新的推理攻击的简短分类法，我们将采用并自定义了几种基本情况。最后，我们用一些相关的隐私保护技术来描述和实验，以减轻此类攻击。
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.
