Risk-Equalized Differentially Private Synthetic Data: Protecting Outliers by Controlling Record-Level Influence

• URL: http://arxiv.org/abs/2602.10232v1
• Date: Tue, 10 Feb 2026 19:22:46 GMT
• Title: Risk-Equalized Differentially Private Synthetic Data: Protecting Outliers by Controlling Record-Level Influence
• Authors: Amir Asiaee, Chao Yan, Zachary B. Abrams, Bradley A. Malin,
• Abstract summary: This paper introduces risk-equalized DP synthesis, a framework that prioritizes protection for high-risk records.<n>Under Gaussian mechanisms, a record's privacy loss is proportional to its influence on the output.<n>Experiments show that risk-weighting substantially reduces membership inference success against high-outlierness records.
• Score: 9.016539021471845
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When synthetic data is released, some individuals are harder to protect than others. A patient with a rare disease combination or a transaction with unusual characteristics stands out from the crowd. Differential privacy provides worst-case guarantees, but empirical attacks -- particularly membership inference -- succeed far more often against such outliers, especially under moderate privacy budgets and with auxiliary information. This paper introduces risk-equalized DP synthesis, a framework that prioritizes protection for high-risk records by reducing their influence on the learned generator. The mechanism operates in two stages: first, a small privacy budget estimates each record's "outlierness"; second, a DP learning procedure weights each record inversely to its risk score. Under Gaussian mechanisms, a record's privacy loss is proportional to its influence on the output -- so deliberately shrinking outliers' contributions yields tighter per-instance privacy bounds for precisely those records that need them most. We prove end-to-end DP guarantees via composition and derive closed-form per-record bounds for the synthesis stage (the scoring stage adds a uniform per-record term). Experiments on simulated data with controlled outlier injection show that risk-weighting substantially reduces membership inference success against high-outlierness records; ablations confirm that targeting -- not random downweighting -- drives the improvement. On real-world benchmarks (Breast Cancer, Adult, German Credit), gains are dataset-dependent, highlighting the interplay between scorer quality and synthesis pipeline.

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