ProxyGuard: Direct Reliability Inference for Randomized Data Release Mechanisms with Shared Targets
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Computer Science > Machine Learning
Title:ProxyGuard: Direct Reliability Inference for Randomized Data Release Mechanisms with Shared Targets
Abstract:Researchers often choose a proxy dataset from many releases, transformations, or seeds. Search can make an invalid release appear adequate, while one adequate release does not establish that its generator is reliable. ProxyGuard controls both errors using prespecified bounded risks and a sealed target set. Named-release mode corrects for multiplicity and certifies specific releases. Direct shared-target mode evaluates independent mechanism draws on a common target, lower-bounds their favorable-score rate, and subtracts a bound on favorable scores contributed by invalid releases. Conditional on the target, release scores are independent, yielding a finite-sample mechanism-reliability guarantee without independent target batches or assumptions on release-level $p$-value dependence. We show that the mean-only penalty is sharp and derive a smooth-score certificate with additive target concentration. In a registered three-requirement study, direct mode raises power from 5.6\% to 64.2\% at reliability 0.95, while named mode remains stronger under high-signal evidence. Prospective audits span full-pipeline Rice--TVAE, which retrains on every draw, and a non-tabular text mechanism.
| Subjects: | Machine Learning (cs.LG); Methodology (stat.ME); Machine Learning (stat.ML) |
| Cite as: | arXiv:2608.18643 [cs.LG] |
| (or arXiv:2608.18643v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.18643
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Dipesh Tharu Mahato [view email][v1] Wed, 19 Aug 2026 07:44:09 UTC (634 KB)
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