athenarc/smpc-analytics

The project is a static academic artifact (M.Sc. thesis) from approximately 2017 with no maintenance or recent activity (0.0 velocity). While it addresses a high-value niche (privacy-preserving medical analytics), its defensibility is effectively zero due to its age and lack of adoption (20 stars). The Privacy-Enhancing Technology (PET) landscape has moved significantly forward since this project's inception, with modern frameworks like MP-SPDZ, OpenMined's PySyft, and Microsoft's SEAL offering more robust, performant, and maintained alternatives. Furthermore, the rise of Confidential Computing (TEEs like Intel SGX or AWS Nitro Enclaves) provides a more commercially viable path for the same 'privacy-preserving' use cases than the pure cryptographic SMPC approach implemented here. Platform domination risk is medium because cloud providers are building these privacy primitives into the infrastructure layer, making standalone research implementations obsolete. It serves as a historical reference but holds no competitive weight in the current market.