Collected molecules will appear here. Add from search or explore.
Privacy-preserving statistical analysis of specific psychological questionnaires (PANAS and BFI-10) using Secure Multi-Party Computation (SMPC) frameworks.
Defensibility
stars
1
The project is a specialized implementation of Secure Multi-Party Computation (SMPC) for specific psychological surveys (PANAS/BFI-10). With only 1 star and no forks after more than three years, it is likely an academic project or a student thesis rather than a production-grade tool. While the use of frameworks like MPyC and JIFF is technically sound, the project lacks a moat because it focuses on a very narrow application (specific questionnaires) rather than building a generalized secure computation engine. Competitors in the privacy space, such as OpenMined (PySyft), Inpher, or Oblivious.ai, provide much more robust, generalized, and well-maintained infrastructures for secure analytics. Frontier labs are unlikely to compete directly as this is too niche, but the project is highly susceptible to displacement by any modern data platform adding basic secure aggregation or differential privacy features. Its value lies purely as a reference for how to apply SMPC to survey logic, but as code, it is effectively stagnant.
TECH STACK
INTEGRATION
reference_implementation
READINESS