Google DeepMind and Google Research have released JAX-Privacy 1.0, a library designed to facilitate differentially private machine learning at scale. This new version, built on the JAX high-performance computing library, offers improved modularity and integrates the latest research advances in privacy-preserving algorithms. JAX-Privacy aims to make it easier for developers and researchers to implement and audit differentially private training pipelines for large datasets and complex models, addressing challenges in scalability and flexibility. AI
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RANK_REASON Release of a new version of an open-source library for differentially private machine learning.
