在药物发现中发布神经网络可能会危及培训数据的隐私,隐私
Fabian P Krüger1,2,3, Johan Östman4, Lewis Mervin5
1Discovery Sciences, Molecular AI, AstraZeneca R&D, Mölndal, 431 83, Sweden. fabian.krueger@tum.de.
Journal of cheminformatics
|March 27, 2025
概括
在机密化学结构上训练的机器学习模型存在重大隐私风险. 基于图形的神经网络可能提供一种解决方案,以保护药物发现中的敏感分子数据.
科学领域:
- 计算化学是一种计算化学.
- 机器学习在药物发现中的作用
- 数据隐私 数据隐私
背景情况:
- 机器学习模型越来越多地用于药物发现.
- 公开分享这些模型可能会暴露机密的化学结构.
- 会员推断攻击是一种隐私评估方法.
研究的目的:
- 调查共享化学结构训练的机器学习模型的隐私风险.
- 评估会员推断攻击在药物发现中的有效性.
- 探索用于保护机密数据的缓解策略.
主要方法:
- 利用会员推断攻击神经网络进行分子性质预测.
- 在各种数据集和架构的黑盒设置中评估隐私风险.
- 研究了组合多次攻击的影响.
- 检查了图形表示和传递信息的神经网络.
主要成果:
- 在所有测试的模型和数据集中都发现了重大隐私风险.
- 结合攻击放大了隐私风险.
- 来自少数阶级的分子被发现特别脆弱.
- 基于图形的分子表示和传递信息的神经网络显示了降低风险的潜力.
结论:
- 在专有化学数据上训练的机器学习模型的共享带来了相当大的隐私问题.
- 仔细考虑数据机密性与模型开放性至关重要.
- 图形神经网络可能为分子建模提供一种更保护隐私的方法.
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