A Differentially Private Weighted Empirical Risk Minimization Procedure and Its Application to Outcome Weighted Learning

This research presents a new algorithm for training predictive models on sensitive data while protecting privacy using differential privacy (DP, a mathematical technique that adds noise to data to prevent identifying individuals). The algorithm extends previous privacy-preserving methods to handle weighted empirical risk minimization (wERM, where different data points contribute differently to model training), which is particularly useful for personalized medical treatment decisions. Testing shows the approach successfully protects privacy while keeping the trained models effective.