Picture the Epsilon: Pursuing Identity-Level Privacy Guarantees for Images
Several methods for auditing privacy in embedding spaces report a number called "epsilon." The common name is misleading: one number may be a heuristic score, another may come from a valid population inequality but ignore sampling uncertainty, and a third may be a confidence bound. This paper asks when such a number is evidence about differential privacy. We study four approaches based on Gaussian calibration, marginal kernel-density ratios, maximum mean discrepancy (MMD), and classifier hypothesis tests. The first two are modeling diagnostics. The MMD and classifier approaches use valid population lower bounds, but only the classifier approach is given a separate test set and a finite-sa...
| Authors | Arman Zareian Jahromi, Vishnu Bondalakunta, Mohammad Akbar Bin Shah et al. |
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| Primary category | cs.CR |
| Published | 17 August 2026 |
| Last updated at source | 4 September 2026 |
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