Differentially Private E-Values

Daniel Csillag, Diego Mesquita
Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, PMLR 300:1999-2007, 2026.

Abstract

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 applications fundamentally rely on sensitive data, which can be leaked through e-values. To ensure their safe release, we propose a general framework for differentially private e-values that transforms any non-private e-value into a differentially private one. Towards this end, we develop a novel biased multiplicative noise mechanism that ensures our differentially private 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 broad applicability.

Cite this Paper

BibTeX
@InProceedings{pmlr-v300-csillag26a, title = { Differentially Private E-Values }, author = {Csillag, Daniel and Mesquita, Diego}, booktitle = {Proceedings of The 29th International Conference on Artificial Intelligence and Statistics}, pages = {1999--2007}, year = {2026}, editor = {Khan, Emtiyaz and Li, Yingzhen and Solin, Arno and Ramdas, Aaditya}, volume = {300}, series = {Proceedings of Machine Learning Research}, month = {02--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v300/main/assets/csillag26a/csillag26a.pdf}, url = {https://proceedings.mlr.press/v300/csillag26a.html}, abstract = { 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 applications fundamentally rely on sensitive data, which can be leaked through e-values. To ensure their safe release, we propose a general framework for differentially private e-values that transforms any non-private e-value into a differentially private one. Towards this end, we develop a novel biased multiplicative noise mechanism that ensures our differentially private 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 broad applicability. } }
Endnote
%0 Conference Paper %T Differentially Private E-Values %A Daniel Csillag %A Diego Mesquita %B Proceedings of The 29th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2026 %E Emtiyaz Khan %E Yingzhen Li %E Arno Solin %E Aaditya Ramdas %F pmlr-v300-csillag26a %I PMLR %P 1999--2007 %U https://proceedings.mlr.press/v300/csillag26a.html %V 300 %X 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 applications fundamentally rely on sensitive data, which can be leaked through e-values. To ensure their safe release, we propose a general framework for differentially private e-values that transforms any non-private e-value into a differentially private one. Towards this end, we develop a novel biased multiplicative noise mechanism that ensures our differentially private 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 broad applicability.
APA
Csillag, D. & Mesquita, D.. (2026). Differentially Private E-Values . Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 300:1999-2007 Available from https://proceedings.mlr.press/v300/csillag26a.html.

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