平衡医疗协作中的隐私与预测性能，提升平台可信度。
Striking the Perfect Balance: Preserving Privacy While Boosting Utility in Collaborative Medical Prediction Platforms
- 提出新型单次分布式学习框架，兼顾患者属性与医生模型隐私。
- 理论证明在特定隐私约束下可实现最优预测性能。
- 在模拟与真实数据上验证了平台的隐私保护与实用效果。
在线协作式医疗预测平台通过利用海量电子健康记录，提供便捷与实时反馈。然而，日益增长的隐私担忧及较低的预测质量可能阻碍患者参与和医生合作。本文首先厘清两类隐私攻击：针对患者的属性攻击与针对医生的模型提取攻击，并明确相应的隐私原则。随后，提出一种隐私保护机制，并集成至新型单次分布式学习框架中，旨在同时满足隐私要求与预测性能目标。基于统计学习理论，我们理论上证明该分布式学习框架在特定隐私约束下可实现最优预测性能。进一步通过玩具仿真与真实数据实验，验证了所开发的隐私保护协作医疗预测平台的有效性。
原文摘要 · Abstract (English)
Online collaborative medical prediction platforms offer convenience and real-time feedback by leveraging massive electronic health records. However, growing concerns about privacy and low prediction quality can deter patient participation and doctor cooperation. In this paper, we first clarify the privacy attacks, namely attribute attacks targeting patients and model extraction attacks targeting doctors, and specify the corresponding privacy principles. We then propose a privacy-preserving mechanism and integrate it into a novel one-shot distributed learning framework, aiming to simultaneously meet both privacy requirements and prediction performance objectives. Within the framework of statistical learning theory, we theoretically demonstrate that the proposed distributed learning framework can achieve the optimal prediction performance under specific privacy requirements. We further validate the developed privacy-preserving collaborative medical prediction platform through both toy simulations and real-world data experiments.
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