DSperse: A Framework for Targeted Verification in Zero-Knowledge Machine Learning
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916955417477120 |
|---|---|
| author | Ivanov, Dan Freiberg, Tristan Shahabi, Shirin Gold, Jonathan Isah, Haruna |
| author_facet | Ivanov, Dan Freiberg, Tristan Shahabi, Shirin Gold, Jonathan Isah, Haruna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06972 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DSperse: A Framework for Targeted Verification in Zero-Knowledge Machine Learning Ivanov, Dan Freiberg, Tristan Shahabi, Shirin Gold, Jonathan Isah, Haruna Artificial Intelligence Cryptography and Security Distributed, Parallel, and Cluster Computing Machine Learning 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. |
| title | DSperse: A Framework for Targeted Verification in Zero-Knowledge Machine Learning |
| topic | Artificial Intelligence Cryptography and Security Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2508.06972 |