提出新方法，精准评估随机数据发布机制的可靠性。
ProxyGuard: Direct Reliability Inference for Randomized Data Release Mechanisms with Shared Targets
- 用共享目标集和预设风险控制误判，避免无效释放被误认有效。
- 直接模式将检验效能从5.6%提升至64.2%，在可靠性0.95时显著增强。
- 适用于需严格审计的生成模型，如文本与非表格数据机制。
研究者常从多个发布版本、变换或随机种子中选择代理数据集。搜索可能导致无效发布被误判为有效，而单一有效发布无法证明生成器可靠。ProxyGuard通过预设的有限风险和密封的目标集，同时控制两类错误。命名发布模式纠正多重性问题，并认证特定发布结果；直接共享目标模式在共同目标上评估独立机制样本，下界其有利得分率，并减去无效发布贡献的上限得分。在目标条件下，发布得分相互独立，无需独立目标批次或对发布层级p值依赖的假设，即可实现有限样本下的机制可靠性保证。我们证明均值惩罚是紧致的，并推导出带加法目标集中度的平滑得分证书。在一项注册的三条件研究中，直接模式使可靠性0.95时的检验功效从5.6%提升至64.2%，而命名模式在强信号证据下仍更优。前瞻性审计涵盖全管线Rice--TVAE（每次抽样重训练）及非表格文本机制。
原文摘要 · Abstract (English)
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.
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