Privacy Cost as Equity Input: A Group Fairness Criterion for Differentially Private Machine Learning
A new preprint introduces the Privacy-Cost Equity Ratio (PCER), a group fairness metric for differentially private machine learning that incorporates disparities in privacy exposure. PCER is calculated by normalizing a group's positive prediction rate by its overfitting gap, which serves as a proxy for vulnerability to membership inference attacks. Evaluations on several benchmarks, including COMPAS, show that protected groups can simultaneously face higher privacy risks and worse predictive outcomes—patterns that standard outcome-only fairness metrics may overlook. The metric is practical for post-hoc audits, requiring only per-group train and test accuracy.
Why it matters: This work broadens the concept of fairness in privacy-preserving machine learning by highlighting the importance of accounting for which groups bear the privacy risks, not just who benefits from model outcomes.
Full story at: arXiv Cryptography and Security ↗