This story draft by @escholar has not been reviewed by an editor, YET.

This paper is available on arxiv under CC BY 4.0 license.

**Authors:**

(1) Zhibo Xing, School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China, and the School of Computer Science, The University of Auckland, Auckland, New Zealand;

(2) Zijian Zhang, School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China, and Southeast Institute of Information Technology, Beijing Institute of Technology, Fujian, China;

(3) Jiamou Liu, School of Computer Science, The University of Auckland, Auckland, New Zealand;

(4) Ziang Zhang, School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China;

(5) Meng Li, Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), Ministry of Education; School of Computer Science and Information Engineering, Hefei University of Technology, 230601 Hefei, Anhui, China; Anhui Province Key Laboratory of Industry Safety and Emergency Technology; and Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology)

(6) Liehuang Zhu, School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, 100081, China;

(7) Giovanni Russello, School of Computer Science, The University of Auckland, Auckland, New Zealand.

Zero-Knowledge Proof-Based Verifiable Machine Learning

Existing Scheme & Challenges and Future Research Directions

Conclusion, Acknowledgment and References

This paper provides a comprehensive review of zeroknowledge proof-based verifiable machine learning (ZKPVML). Firstly, we introduce machine learning and zeroknowledge proofs separately, highlighting the computational characteristics of machine learning and the advantages of zero-knowledge proof technology over other cryptographic techniques. We then provide a formal definition of ZKPVML and extract three important properties of ZKP-VML schemes. Additionally, we outline the two major challenges faced by ZKP-VML. Subsequently, we categorize and analyze existing work from three perspectives: technical approaches, performance evaluation, and implementations, based on their features and construction methods. We also point out some construction techniques used in these schemes. Furthermore, we conduct a comparative analysis of zero-knowledge proof toolkits through experiments and provide implementation recommendations for ZKP-VML schemes. Finally, we discuss the challenges and future research directions in ZKP-VML. We believe that this work will inspire further research into the field of ZKP-VML.

This work is supported by National Natural Science Foundation of China (NSFC) under the grant No. 62172040, No. 61872041, No. U1836212, and National Key Research and Development Program of China under the grant No.2021YFB2701200, 2022YFB2702402.

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