Hierarchical Batch Optimized Merkle Trees: An Efficient Blockchain Based Approach for Credential Revocation in SSI System

Blockchain based credential revocation strategies presently require excessive system computation and excessive system storage and generate substantial cryptographic evidence during multimillion credential transactions. A new Hierarchical Batch Optimized Merkle Tree system, known as HBMT, presents a credential revocation solution for Self Sovereign Identity (SSI) systems to resolve present scalability and efficiency limitations of current methods according to this work. Our solution arranges credentials through hierarchical multilevel groups formed by a natural organizational structure that reduces both processing and verification times. The semantic categorization structure of HBMT differs from flat Merkle trees by structuring credentials according to semantic groups, thus minimizing the impact of updates throughout the system. Natural organizational relationships across credential ecosystems within HBMT produce dual advantages of decreased computation and storage requirements, together with superior verification system performance. The research evaluates the scalability of Self Sovereign Identity deployments which require both performance gains and cost-effective solutions for broad market acceptance.