2025/10/21 by Daniel Csillag, Csillag, Daniel, Diego Mesquita +1
Computer Science · #Adversarial Robustness in Machine Learning #Cryptography and Security (cs.CR) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Multiplicative function #Noise (video) #Privacy-Preserving Technologies in Data #Statistical analysis #Statistical inference #Statistical model
paper · pdf · doi:10.48550/arxiv.2510.18654
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/10/21 · openalex created_date 2025/10/24 · openalex updated_date 2026/08/05
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.