arXiv:2606.09582v1 Announce Type: new Abstract: Recent work argues for using Gaussian differential privacy (GDP) to report the privacy guarantees in privacy-preserving machine learning. We provide principled mappings from pure-DP $\varepsilon$ to GDP $\mu$ by matching the worst-case success of a strong-adversary membership inference attack in terms of three metrics: multiplicative advantage at fixed FPR, precision at fixed recall, and the standard privacy profile. We tabulate $\mu$ values across a useful range of parameters and recommend $\mu \approx \varepsilon/5$ as a conservative general-pu
This paper addresses a current challenge in privacy-preserving machine learning, moving from theoretical guarantees to practical, comparable metrics for Gaussian Differential Privacy.
Establishing clear, principled mappings for privacy parameters is crucial for the adoption and trustworthiness of privacy-preserving AI systems, affecting regulatory compliance and public acceptance.
The ability to more accurately quantify and compare privacy guarantees (epsilon to mu) under different differential privacy frameworks provides a standardized methodology for ML practitioners.
- · AI developers
- · Privacy-enhancing technologies sector
- · Data privacy regulators
- · Adversarial attackers
- · Organizations with weak privacy practices
Improved standardization and comparability of privacy guarantees in machine learning models through a clearer understanding of Gaussian Differential Privacy's parameters.
Increased adoption of differential privacy in commercial machine learning applications due to better interpretability and regulatory compliance.
Potential for new privacy-preserving AI products and services that can confidently articulate their privacy guarantees to users and authorities.
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Read at arXiv cs.LG