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[2602.17861] JAX-Privacy: A library for differentially private machine learning
Summary
JAX-Privacy is a new library aimed at simplifying the implementation of differentially private machine learning, offering both customization for researchers and ease of use for practitioners.
Why It Matters
As privacy concerns grow in machine learning applications, JAX-Privacy addresses the need for robust mechanisms that ensure data confidentiality. This library enhances the accessibility of differential privacy techniques, making it easier for developers and researchers to adopt these important practices in their work.
Key Takeaways
- JAX-Privacy simplifies the deployment of differentially private ML mechanisms.
- The library is designed for both deep customization and out-of-the-box usability.
- It includes verified, modular components for critical aspects of mechanism design.
- JAX-Privacy integrates recent research findings in differential privacy.
- The library aims to enhance both the efficiency and flexibility of privacy-preserving ML.
Computer Science > Machine Learning arXiv:2602.17861 (cs) [Submitted on 19 Feb 2026] Title:JAX-Privacy: A library for differentially private machine learning Authors:Ryan McKenna, Galen Andrew, Borja Balle, Vadym Doroshenko, Arun Ganesh, Weiwei Kong, Alex Kurakin, Brendan McMahan, Mikhail Pravilov View a PDF of the paper titled JAX-Privacy: A library for differentially private machine learning, by Ryan McKenna and 8 other authors View PDF HTML (experimental) Abstract:JAX-Privacy is a library designed to simplify the deployment of robust and performant mechanisms for differentially private machine learning. Guided by design principles of usability, flexibility, and efficiency, JAX-Privacy serves both researchers requiring deep customization and practitioners who want a more out-of-the-box experience. The library provides verified, modular primitives for critical components for all aspects of the mechanism design including batch selection, gradient clipping, noise addition, accounting, and auditing, and brings together a large body of recent research on differentially private ML. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2602.17861 [cs.LG] (or arXiv:2602.17861v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.17861 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ryan McKenna [view email] [v1] Thu, 19 Feb 2026 21:55:05 UTC (41 KB) Full-text links: Access Paper: View a PDF of the paper titled JAX-Priva...
