tpmmthomas/secure-data-valuation

The project is a specialized academic implementation addressing the intersection of data valuation (identifying which training data is most valuable) and zero-knowledge proofs (proving properties of data without revealing the data itself). While the conceptual approach of 'Secure Data Valuation' is highly relevant for future decentralized data marketplaces and privacy-preserving AI, the repository itself has zero stars, forks, or velocity after more than a year. This indicates it is a 'zombie' reference implementation for a paper rather than a living software project. From a competitive standpoint, it lacks any moat beyond the specific mathematical proofs derived in the associated paper. It faces stiff competition from broader zkML (zero-knowledge machine learning) frameworks like EZKL, Modulus Labs, or Giza, which are building general-purpose infrastructure that could eventually encompass these specific data valuation algorithms. The low defensibility score reflects the lack of adoption and the ease with which a well-funded zkML startup could replicate or improve upon this specific niche capability.