Striking the Perfect Balance: Preserving Privacy While Boosting Utility in Collaborative Medical Prediction Platforms
This work addresses privacy and prediction quality issues in medical platforms to encourage patient and doctor participation, though it appears incremental as it builds on existing distributed learning methods.
The paper tackles the challenge of balancing privacy and utility in collaborative medical prediction platforms by proposing a privacy-preserving mechanism integrated into a one-shot distributed learning framework, achieving optimal prediction performance under specific privacy requirements as validated through simulations and real-world data.
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.