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

2023-07-24 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, quick

Summary

The paper "A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning" (arXiv:2307.13127, last revised 22 Jun 2026) introduces the first differentially private (DP) algorithm for general weighted Empirical Risk Minimization (wERM). This addresses a gap where prior DP research primarily focused on unweighted ERM, despite wERM's importance for models where individual data contributions vary. The proposed DP-wERM procedure offers formal privacy guarantees and derives both empirical and population excess risk bounds. Crucially, this framework enables privacy-preserving learning for individualized treatment rules, including the popular Outcome-Weighted Learning (OWL) approach. Experiments on simulated and real data confirm that DP-wERM applied to OWL maintains robust performance while providing strong DP guarantees, making it practical for sensitive real-world datasets.

Key takeaway

For AI Scientists developing models with sensitive personal data, particularly those using weighted Empirical Risk Minimization (wERM) or Outcome-Weighted Learning (OWL), you should consider integrating this new differentially private wERM algorithm. This method offers mathematically provable privacy bounds without significantly compromising model performance, enabling robust and ethical deployment of individualized treatment rules. Evaluate its application to your specific datasets to ensure compliance and data protection.

Key insights

A new differentially private algorithm extends privacy guarantees to weighted empirical risk minimization for sensitive data.

Principles

• Differential privacy can be extended to weighted learning.
• Formal privacy guarantees are achievable for wERM.

Method

The procedure involves applying differential privacy to general weighted empirical risk minimization, deriving empirical and population excess risk bounds, and demonstrating its use in Outcome-Weighted Learning.

In practice

• Apply DP-wERM to protect individualized treatment rules.
• Use DP-wERM for sensitive real-world datasets.

Topics

• Differential Privacy
• Weighted Empirical Risk Minimization
• Outcome-Weighted Learning
• Individualized Treatment Rules
• Privacy-Preserving Machine Learning
• Statistical Machine Learning

Best for: AI Scientist, Research Scientist, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.