Practical Privacy-Preserving Gaussian Process Regression via Secret Sharing

The project is an academic reference implementation for a 2023 paper. With 0 stars and 7 forks over nearly three years, it lacks any developer ecosystem or production traction. While the application of Secret Sharing (SS) to Gaussian Process Regression is technically non-trivial due to the $O(n^3)$ complexity of matrix operations in a secure context, the project remains a research artifact rather than a tool. Defensibility is very low because the value lies in the mathematical proofs and algorithmic steps described in the paper, which can be re-implemented by any competent cryptography engineer in more robust frameworks like MP-SPDZ or CrypTen. Frontier labs face low risk from this as they are focused on scaling Transformers, not optimizing niche non-parametric Bayesian models like GPR. The primary threat to this project is obsolescence by general-purpose privacy-preserving ML libraries (e.g., PySyft, OpenMined) or the shift toward Differential Privacy over SMPC for high-dimensional data.