arXiv:2605.28952v1 Announce Type: cross Abstract: E-values have attracted considerable interest in recent years as flexible tools for enabling anytime-valid and adaptive data analysis. Hypothesis testing is at the core of many of these applications, which can often involve private or sensitive data. In this work, we answer a simple but important question: given two distributions $\mathbb{P}$ and $\mathbb{Q}$, what is the maximum achievable e-power when testing $X\sim \mathbb{P}^n$ against $X\sim\mathbb{Q}^n$ with e-values that satisfy $\varepsilon$-differential privacy? We characterize the opt
The increasing use of AI in sensitive data applications necessitates robust privacy guarantees, making research into differentially private hypothesis testing timely.
This research provides foundational advancements for AI applications involving private data, crucial for areas like healthcare, finance, and secure AI model training.
The ability to formally characterize optimal e-power under differential privacy could lead to more reliable and trustworthy privacy-preserving AI systems.
- · AI researchers
- · Data privacy advocates
- · Sectors using sensitive data
- · Malicious data actors
- · Systems without strong privacy guarantees
Improved theoretical understanding of privacy-preserving statistical inference.
Development of new algorithms and tools for privacy-preserving AI and data analysis.
Increased trust and adoption of AI in highly regulated industries and applications involving personal data.
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