# Engineering Trustworthy Machine-Learning Operations with Zero-Knowledge Proofs

arXiv: https://arxiv.org/abs/2505.20136 · HTML: https://mlsanity.com/paper/2505.20136/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2025.

## Built on by (most cited first, 3 of 3)

- [DSperse: A Framework for Targeted Verification in Zero-Knowledge Machine Learning](https://mlsanity.com/paper/2508.06972/) (2025)
- [Blockchain and AI: Securing Intelligent Networks for the Future](https://mlsanity.com/paper/2604.06323/) (2026)
- [When Agentic Trust Crosses Organizational Boundaries: Structural Externalization and a Reference Model for Trust Evidence](https://mlsanity.com/paper/2609.22961/) (2026)

## Abstract (arXiv)

As Artificial Intelligence (AI) systems, particularly those based on machine learning (ML), become integral to high-stakes applications, their probabilistic and opaque nature poses significant challenges to traditional verification and validation methods. These challenges are exacerbated in regulated sectors requiring tamper-proof, auditable evidence, as highlighted by apposite legal frameworks, e.g., the EU AI Act. Conversely, Zero-Knowledge Proofs (ZKPs) offer a cryptographic solution that enables provers to demonstrate, through verified computations, adherence to set requirements without revealing sensitive model details or data. Through a systematic survey of ZKP protocols, we identify five key properties (non-interactivity, transparent setup, standard representations, succinctness, and post-quantum security) critical for their application in AI validation and verification pipelines. Subsequently, we perform a follow-up systematic survey analyzing ZKP-enhanced ML applications across an adaptation of the Team Data Science Process (TDSP) model (Data & Preprocessing, Training & Offline Metrics, Inference, and Online Metrics), detailing verification objectives, ML models, and adopted protocols. Our findings indicate that current research on ZKP-Enhanced ML primarily focuses on inference verification, while the data preprocessing and training stages remain underexplored. Most notably, our analysis identifies a significant convergence within the research domain toward the development of a unified Zero-Knowledge Machine Learning Operations (ZKMLOps) framework. This emerging framework leverages ZKPs to provide robust cryptographic guarantees of correctness, integrity, and privacy, thereby promoting enhanced accountability, transparency, and compliance with Trustworthy AI principles.