标题： 差分隐私的E值 显示英文标题

标题： Differentially Private E-Values

摘要： E值作为统计推断和风险控制的灵活工具得到了重视，在最少假设下能够实现任何时间点和事后有效的程序。 然而，许多现实世界的应用本质上依赖于敏感数据，这些数据可能通过E值泄露。 为了确保它们的安全发布，我们提出一个通用框架，将非私有的E值转换为差分隐私的E值。 为此，我们开发了一种新颖的有偏乘法噪声机制，确保我们的E值保持统计有效性。 我们证明，我们的差分隐私E值具有强大的统计功效，并且在渐近意义上与非私有的对应项同样强大。 在在线风险监控、隐私医疗保健和共形E预测中的实验展示了我们方法的有效性，并说明了其广泛适用性。 显示英文摘要

摘要： 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.