ZKPROV: A Zero-Knowledge Approach to Dataset Provenance for Large Language Models

ZKPROV addresses a high-value problem in regulated industries: proving model lineage without compromising data privacy. From a technical perspective, Zero-Knowledge Machine Learning (ZKML) is an exceptionally high-barrier-to-entry field. However, the project's current state is a research artifact (0 stars, 3 forks, nearly a year old) rather than a production-grade library. Its defensibility is purely based on the complexity of the cryptographic implementation rather than market position or community. Frontier labs like OpenAI are unlikely to prioritize this because their business model relies on opaque data moats, not verifiable provenance. The primary competition comes from dedicated ZKML startups like Modulus Labs or EZKL, who are building more generalized toolsets. The specific challenge for ZKPROV is the computational overhead of ZKPs; proving a full LLM training run is currently several orders of magnitude more expensive than the training itself. Until the 'ZK tax' is reduced, this remains a theoretical or niche regulatory tool.