AlekseiTriastcyn/bayesian-differential-privacy

This project is a classic 'code-behind-the-paper' repository. While the theoretical approach—using Bayesian inference to potentially provide tighter privacy bounds than traditional (epsilon, delta)-DP—is scientifically interesting, the repository itself is dormant. With only 23 stars and zero velocity over nearly six years, it has failed to transition from a research artifact to a usable tool. In the competitive landscape of privacy-preserving machine learning (PPML), dominance has been captured by production-grade libraries like Meta's Opacus, Google's DP-TensorFlow, and Microsoft's SmartNoise. These libraries focus on the standard DP definitions which have much broader industry consensus. The lack of recent updates means it likely doesn't support modern ML framework versions (e.g., PyTorch 2.x), making it a legacy reference for researchers rather than a building block for developers. Platform risk is low because frontier labs are unlikely to adopt this specific Bayesian flavor of DP, preferring the established standards they have already integrated into their stacks.