Privacy-Preserving and Scalable Blockchain-Based Certificate Authentication Using Zero-Knowledge Rollups and AI-Based Trust Scoring

Abstract

The accelerated digitalization of academic qualifications requires certificate authentication systems with not only the capability not to be tampered with, but also privacy-assuring and scalable. Although certificate verification using blockchain guarantees immutability and transparency, current solutions have high transaction costs, low scalability, and a loosely applied guarantee of privacy. In this research, proposes a new Privacy-Preserving and Scalable Blockchain-Based Certificate Authentication System, which combines Zero-Knowledge (ZK) rollups with an AI-based system of trust scoring. ZK-rollups save a lot of gas through batching certificate transactions and generating succinct cryptographic proofs, which enhances throughput and makes operational costs less. Zero-knowledge proofs can be used to facilitate privacy by providing the selective disclosure so that the verifiers can verify the validity of the certificates without access to sensitive personal information. Also, trust scoring model is an AI-based model that dynamically analyzes the actions of the validators to identify anomalies, collusion, and malicious actions. The experimental analysis shows significant reduction in transaction costs (as much as 80 percent), lower verification latency as well as resilience against Sybil and coordinated attacks. The suggested model provides a privacy-conscious, secure and scalable decentralized certificate authentication model.