google Differentially private machine learning at scale with JAX-Privacy (opens in new tab)
Google DeepMind and Google Research have announced the release of JAX-Privacy 1.0, a high-performance library designed to scale differentially private (DP) machine learning. By leveraging JAX’s native parallelization and functional programming model, the toolkit enables researchers to train large-scale foundation models while maintaining rigorous privacy guarantees. This version introduces modular components for advanced algorithms and empirical auditing, making private training both computationally efficient and verifiable across distributed environments. ### Scaling Differential Privacy with JAX * The library is built directly on the JAX ecosystem, integrating seamlessly with Flax for neural network architectures and Optax for optimization. * It utilizes JAX’s `vmap` for automatic vectorization and `shard_map` for single-program multiple-data (SPMD) parallelization, allowing DP primitives to scale across multiple accelerators. * By using just-in-time (JIT) compilation, the library mitigates the traditional performance overhead associated with per-example gradient clipping and noise addition. ### Core Components and Advanced Algorithms * The toolkit provides fundamental building blocks for implementing standard DP algorithms like DP-SGD and DP-FTRL, including specialized modules for data batch construction. * It supports state-of-the-art methods such as DP matrix factorization, which improves performance by injecting correlated noise across training iterations. * Features like micro-batching and padding are included to handle the massive, variable-sized batches often required to achieve an optimal balance between privacy and model utility. ### Verification and Privacy Auditing * JAX-Privacy incorporates rigorous privacy accounting based on Rényi Differential Privacy to provide precise tracking of privacy budgets. * The library includes tools for empirical auditing, allowing developers to validate their privacy guarantees through techniques like membership inference attacks and data poisoning. * The design ensures correctness in distributed settings, specifically focusing on consistent noise generation and gradient synchronization across clusters. JAX-Privacy 1.0 is a robust solution for researchers and engineers who need to deploy production-grade private models. Its modular architecture and integration with high-performance computing primitives make it a primary choice for training foundation models on sensitive datasets without compromising on scalability or security.