Striking the Perfect Balance: Preserving Privacy While Boosting Utility in Collaborative Medical Prediction Platforms

TL;DR

This paper introduces a privacy-preserving distributed learning framework for collaborative medical prediction that balances patient privacy with high prediction accuracy, validated through theoretical analysis and real-world experiments.

Contribution

It proposes a novel one-shot distributed learning mechanism that ensures privacy and utility in medical prediction platforms, addressing key privacy attack vulnerabilities.

Findings

Achieves optimal prediction performance under privacy constraints

Effectively defends against attribute and model extraction attacks

Validated with real-world medical data experiments

Abstract

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.