提出隐私保护的e值生成方法，确保敏感数据安全下仍保持统计有效性。
Differentially Private E-Values
- 设计新型有偏乘性噪声机制，实现e值的差分隐私转换。
- 在多个场景中保持强统计功效，渐近性能接近非私有版本。
- 适合医疗、在线风险监控等需隐私保护的实时统计推断任务。
e值作为灵活的统计推断与风险控制工具，可在极少假设下实现任意时间及事后有效的分析。然而，许多实际应用依赖敏感数据，e值本身可能造成数据泄露。为确保安全发布，本文提出一种通用框架，将非私有的e值转化为差分隐私版本。为此，我们设计了一种新颖的有偏乘性噪声机制，保证转换后的e值在统计上依然有效。实验表明，所提出的差分隐私e值具有强大的统计功效，在在线风险监测、隐私医疗和置信区间预测等任务中表现优异，展示了其广泛适用性。
原文摘要 · Abstract (English)
E-values have gained prominence as flexible tools for statistical inference and risk control, enabling anytime- and post-hoc-valid procedures under minimal assumptions. However, many real-world applications fundamentally rely on sensitive data, which can be leaked through e-values. To ensure their safe release, we propose a general framework to transform non-private e-values into differentially private ones. Towards this end, we develop a novel biased multiplicative noise mechanism that ensures our e-values remain statistically valid. We show that our differentially private e-values attain strong statistical power, and are asymptotically as powerful as their non-private counterparts. Experiments across online risk monitoring, private healthcare, and conformal e-prediction demonstrate our approach's effectiveness and illustrate its broad applicability.
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