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

arXiv: https://arxiv.org/abs/2508.06972 · HTML: https://mlsanity.com/paper/2508.06972/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 14 references that are in the map.
- First posted on arXiv 9 Aug 2025.

## Builds on (most cited first, 8 of 8)

- [SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud](https://mlsanity.com/paper/1706.10268/) (2017)
- [A Survey of Zero-Knowledge Proof Based Verifiable Machine Learning](https://mlsanity.com/paper/2502.18535/) (2025)
- [Zero-Knowledge Proof-based Verifiable Decentralized Machine Learning in Communication Network: A Comprehensive Survey](https://mlsanity.com/paper/2310.14848/) (2023)
- [Dynamic Slicing for Deep Neural Networks](https://mlsanity.com/paper/2009.13747/) (2020)
- [Verifiable evaluations of machine learning models using zkSNARKs](https://mlsanity.com/paper/2402.02675/) (2024)
- [Zero-Knowledge Proof Frameworks: A Systematic Survey](https://mlsanity.com/paper/2502.07063/) (2025)
- [Engineering Trustworthy Machine-Learning Operations with Zero-Knowledge Proofs](https://mlsanity.com/paper/2505.20136/) (2025)
- [NeuSemSlice: Towards Effective DNN Model Maintenance via Neuron-level Semantic Slicing](https://mlsanity.com/paper/2407.20281/) (2024)

## Built on by (most cited first, 1 of 1)

- [Beyond Training: A Feasibility Taxonomy for Inference-Time AI Governance](https://mlsanity.com/paper/2609.10105/) (2026)

## Abstract (arXiv)

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.