Research on Privacy Protection Adaptation and Cross-Domain Application of Zero-Knowledge Proof Technology

Fan Gao

doi:10.47297/taposatWSP2633-456909.20260702

PDF
Export
Share
Collection
Album

Research on Privacy Protection Adaptation and Cross-Domain Application of Zero-Knowledge Proof Technology

Volume 7, Issue 2

Views: 0 Downloads: 0

Research on Privacy Protection Adaptation and Cross-Domain Application of Zero-Knowledge Proof Technology
Research on Privacy Protection Adaptation and Cross-Domain Application of Zero-Knowledge Proof Technology
College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, Fujian, 350117, China
Vol. 7, Issue 2, Pages: 44-51(2026)
Published：28 February 2026
DOI：10.47297/taposatWSP2633-456909.20260702
Accepted：

Scan QR Code

Fan Gao.Research on Privacy Protection Adaptation and Cross-Domain Application of Zero-Knowledge Proof Technology[J].Theory and Practice of Science and Technology,2026,07(02):44-51.
DOI：

Fan Gao.Research on Privacy Protection Adaptation and Cross-Domain Application of Zero-Knowledge Proof Technology[J].Theory and Practice of Science and Technology,2026,07(02):44-51. DOI： 10.47297/taposatWSP2633-456909.20260702.

摘要

Abstract

In the digital economy era

the contradiction between data sharing and privacy protection is increasingly prominent. Traditional privacy protection technologies such as anonymization

differential privacy

and homomorphic encryption are difficult to meet the diverse privacy demands in multiple fields due to their flaws such as vulnerability to cracking

sacrificing data accuracy

and high computational complexity. Zero-knowledge proof (ZKP)

with its core advantage of "data available but not visible"

has become the key technical support to solve this contradiction. However

the current protocol types are complex and the demands in various fields vary significantly

leading to confusion in protocol selection and imbalance in resource allocation in practice. This paper systematically reviews the theoretical basis

technical system and mainstream protocol characteristics of zero-knowledge proof

integrates the advantages of demand-driven decision tree and protocol three-dimensional classification model

constructs a protocol selection framework of "demand-driven - feature matching - dynamic optimization"

and clarifies the multi-stage decision-making process; Four typical fields

namely education

machine learning

finance

and healthcare

were selected to verify the effectiveness and universality of the framework

providing standardized tools for the large-scale application of zero-knowledge proof technology in various fields and direction guidance for the development of privacy protection technology in the post-quantum era.

关键词

Keywords

references

Goldwasser, S., Micali, S., & Rackoff, C. (1989). The knowledge complexity of interactive proof systems. SIAM Journal on Computing, 18(1), 186–208.

Goldreich, O., Micali, S., & Wigderson, A. (1991). Proofs that yield nothing but their validity. Journal of the ACM, 38(3), 691–729.

Groth, J. (2016). Efficient zero-knowledge proofs for blockchain. Zcash.

StarkWare. (2018). STARKs: Scalable transparent arguments of knowledge. StarkWare Industries.

Matter Labs. (2024). zk-Rollup: Scaling Ethereum with zero-knowledge proofs. Matter Labs.

Chiesa, A., Ojha, D., & Spooner, N. (2020). Marlin: Efficient zero-knowledge proofs with incremental verification. In Proceedings of the 39th Annual International Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT 2020) (pp. 355–384). Springer.

Lyubashevsky, V., Nguyen, N. K., & Seiler, G. (2023). Lattice-based zero-knowledge proofs for post-quantum cryptography. In Proceedings of the 43rd Annual International Cryptology Conference (CRYPTO 2023) (pp. 456–485). Springer.

IACR. (2023). Do you need a zero knowledge proof? (IACR Cryptology ePrint Archive Report No. 2023/1234). https://eprint.iacr.org/2023/1234.

Wang, X., et al. (2024). A taxonomy of zero-knowledge proofs for privacy-preserving systems. In Proceedings of the 33rd USENIX Security Symposium (pp. 567–584). USENIX Association.

Oasis Network. (2021). Dynamic zero-knowledge proofs for medical data sharing. Oasis Labs.

Zcash Foundation. (2016). Zcash protocol specification. Zcash Foundation.

Li, Y., et al. (2024). Dynamic zero-knowledge proofs for blockchain state transitions. In Proceedings of the 31st ACM SIGSAC Conference on Computer and Communications Security (CCS 2024) (pp. 1234–1251). ACM.