Research on Zero knowledge with machine learning
DOI:
https://doi.org/10.54097/6awase9wKeywords:
Zero knowledge, Machine learning, Zero-knowledge proofsAbstract
This research paper explores the intersection of zero-knowledge proofs (ZKPs) and machine learning (ML), presenting a comprehensive overview of recent advancements, applications, and challenges in this fast growing area. The jointers of ZKPs and ML techniques shall go a meter further to fuse privacy, security, and integrity in a number of solutions, which include forming of groups for data sharing and safe machine learning. Through the investigation of the well-respected sites in that area and also the thorough description of formulas and their experimental outcome, this paper looks for the clarification of the current state of affairs and the possible future directions of ZKPs in the AI world. By inserting the verification mechanism of ZKPs into machine learning ecosystem, it allows devising novel solutions for the problems of privacy and confidentiality that have for long been not solved. With this approach, the concatenation of parties collectively performs the process of dealing with private inputs without revealing any of these data and this, in return, opens the possibilities of secure multi-party computation. Furthermore, ZKPs protect data sharing as it gives people the opportunity to construct confidential data and share them to model training without compromising any one’s private details. Being a part of the dynamic conversations, which focus on the game-changing capacity of transparent zero-knowledge proofs (ZKPs), this paper brings the role of ZKPs in preserving the confidentiality and integrity of artificial intelligence (AI) applications into the centre of attention. As scientists still fight to improve protocols and circumvent computational complications, ZKPs are likely to establishment as critical tools in the effort to increase ML systems in the digital sphere.
References
Christ, M., Baldimtsi, F., Chalkias, K.K., Maram, D., Roy, A. and Wang, J., 2024. SoK: Zero-Knowledge Range Proofs. Cryptology ePrint Archive.
Ernstberger, J., Chaliasos, S., Zhou, L., Jovanovic, P. and Gervais, A., 2024. Do You Need a Zero Knowledge Proof?. Cryptology ePrint Archive.
Laufer, E., Ozdemir, A. and Boneh, D., 2024. zkPi: Proving Lean Theorems in Zero-Knowledge. Cryptology ePrint Archive.
Li, D., Ke, X., Zhang, X. and Zhang, Y., 2024. A trusted and regulated data trading scheme based on blockchain and zero‐knowledge proof. IET Blockchain.
Salam, A., Abrar, M., Amin, F., Ullah, F., Khan, I.A., Alkhamees, B.F. and Alsalman, H., 2024. Securing Smart Manufacturing by Integrating Anomaly Detection with Zero-Knowledge Proofs. IEEE Access.
Singh, S., 2024, March. Enhancing Privacy and Security in Large-Language Models: A Zero-Knowledge Proof Approach. In International Conference on Cyber Warfare and Security (Vol. 19, No. 1, pp. 574-582).
Wellington, S., 2024. BasedAI: A decentralized P2P network for Zero Knowledge Large Language Models (ZK-LLMs). arXiv preprint arXiv:2403.01008.
Xin, J., Haghighi, A., Tian, X. and Papadopoulos, D., 2024. Notus: Dynamic Proofs of Liabilities from Zero-knowledge RSA Accumulators. Cryptology ePrint Archive.
Zhou, L., Diro, A., Saini, A., Kaisar, S. and Hiep, P.C., 2024. Leveraging zero knowledge proofs for blockchain-based identity sharing: A survey of advancements, challenges and opportunities. Journal of Information Security and Applications, 80, p.103678.
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