Artificial Intelligence in Zero-Knowledge Proofs: Transforming Privacy in Cryptographic Protocols
DOI:
https://doi.org/10.18034/ei.v12i1.743Keywords:
Artificial Intelligence, Zero-Knowledge Proofs, Cryptographic Protocols, Privacy-Preserving Systems, Data Privacy, Cryptographic SecurityAbstract
AI and zero-knowledge proofs (ZKPs) may revolutionize cryptographic protocol privacy, as this research shows. The report examines how AI may improve ZKP efficiency, scalability, and security and identifies developing AI-driven privacy-preserving technologies across sectors. The study reviews secondary data from peer-reviewed journals, technical reports, and conference proceedings. Key results show that AI automates proof creation, optimizes verification procedures, and identifies weaknesses, allowing innovative architectures like federated learning mixed with ZKPs for safe, collaborative AI training. The research shows AI's potential to improve privacy in banking, healthcare, and secure identity management. However, concerns about the computational needs of the AI model, explainable systems, and interoperability persist. The policy implications highlight standardization, security framework improvements, and research to solve these shortcomings. The policy should also support openness and accountability in AI-driven cryptography systems to build confidence and acceptance. This paper shows how AI might transform privacy-preserving cryptographic methods and how to overcome their existing limitations to maximize their promise.
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Copyright (c) 2024 Abhishake Reddy Onteddu; Samuel Koehler; RamMohan Reddy Kundavaram; Krishna Devarapu; Srinikhita Kothapalli; Deekshith Narsina
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