DSperse: A Framework for Targeted Verification in Zero-Knowledge Machine Learning

• URL: http://arxiv.org/abs/2508.06972v3
• Date: Thu, 18 Sep 2025 03:15:13 GMT
• Title: DSperse: A Framework for Targeted Verification in Zero-Knowledge Machine Learning
• Authors: Dan Ivanov, Tristan Freiberg, Shirin Shahabi, Jonathan Gold, Haruna Isah,
• Abstract summary: DSperse is a framework for distributed machine learning inference with cryptographic verification.<n>We evaluate DSperse using multiple proving systems and report empirical results on memory usage, runtime, and circuit behavior.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: DSperse is a modular framework for distributed machine learning inference with strategic cryptographic verification. Operating within the emerging paradigm of distributed zero-knowledge machine learning, DSperse avoids the high cost and rigidity of full-model circuitization by enabling targeted verification of strategically chosen subcomputations. These verifiable segments, or "slices", may cover part or all of the inference pipeline, with global consistency enforced through audit, replication, or economic incentives. This architecture supports a pragmatic form of trust minimization, localizing zero-knowledge proofs to the components where they provide the greatest value. We evaluate DSperse using multiple proving systems and report empirical results on memory usage, runtime, and circuit behavior under sliced and unsliced configurations. By allowing proof boundaries to align flexibly with the model's logical structure, DSperse supports scalable, targeted verification strategies suited to diverse deployment needs.

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