Navigating the privacy-accuracy tradeoff: federated survival analysis with binning and differential privacy

Abstract

Federated Learning (FL) offers a decentralized approach to model training, allowing for data-driven insights while safeguarding patient privacy across institutions. In the Personal Health Train (PHT) paradigm, it is the local model gradients from each institution, aggregated over a sample size of its own patients that are transmitted to a central server to be globally merged, rather than transmitting the patient data itself. However, certain attacks on a PHT infrastructure may risk compromising sensitive data. This study delves into the privacy-accuracy tradeoff in federated Cox Proportional Hazards (CoxPH) models for survival analysis by assessing two Privacy-Enhancing Techniques (PETs) added on top of the PHT approach. In one, we implemented a Discretized Cox model by grouping event times into finite bins to hide individual time-to-event data points. In another, we explored Local Differential Privacy by introducing noise to local model gradients. Our results show that both strategies can reduce these privacy risks without significantly compromising numerical accuracy, reflected in small variations of the hazard ratios and, more noticeably, in the cumulative baseline hazard curves during late follow-up. Our findings highlight the potential for enhancing privacy-preserving survival analysis within a PHT implementation and suggest practical options for multi-institutional research while reducing the exposure that could enable re-identification.