Zero-knowledge Proof Meets Machine Learning in Verifiability: Conclusion

TABLE OF LINKS

Abstract & Introduction

Background

Zero-Knowledge Proof-Based Verifiable Machine Learning

Existing Scheme & Challenges and Future Research Directions

Conclusion, Acknowledgment and References

VI. CONCLUSION

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.

ACKNOWLEDGMENT

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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