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Evidence record 11937 · automatically gathered

Privacy Cost as Equity Input: A Group Fairness Criterion for Differentially Private Machine Learning

arXiv:2607.16620v1 Announce Type: cross Abstract: Differential privacy (DP) is increasingly deployed to limit membership inference risk in machine-learning systems. Prior work has shown that DP-SGD can widen accuracy disparities across demographic groups, but this framing treats fairness as a purely outcome-side concern. We argue that privacy cost, the information leakage borne by each group, is itself a form of harm, and adopt a compensatory-fairness framework in which a group that involuntaril

Record details

Published: 21 July 2026
Source: arXiv cs.CY
Category: Research
Topics: Bias & fairness · Privacy
Retrieved: 21 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Same publisher byline · Shared topic: Bias & fairness

Privacy Cost as Equity Input: A Group Fairness Criterion for Differentially Private Machine Learning

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How to cite this record

ethics.ai (21 July 2026), “Privacy Cost as Equity Input: A Group Fairness Criterion for Differentially Private Machine Learning,” evidence record 11937, https://ethics.ai/record/11937 (originally published by arXiv cs.CY).

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