A novel lightweight medical blockchain data query scheme

Area of Science:

• Computer Science
• Blockchain Technology
• Medical Informatics

Background:

• Lightweight medical blockchains face challenges with data query efficiency and nonexistence proofs.
• Existing schemes lack optimized storage and robust verification mechanisms.
• Resource-constrained environments like mobile healthcare require efficient data management solutions.

Purpose of the Study:

• To propose a novel lightweight medical blockchain data query scheme.
• To enhance data query efficiency and accuracy of nonexistence proofs.
• To improve system security and suitability for resource-constrained scenarios.

Main Methods:

• Utilized XGBoost algorithm for predicting medical data weights and optimizing storage architecture.
• Developed an efficient query method combining aggregated Bloom filters and Merkle-Huffman (MH) trees.
• Implemented a multi-node collaborative verification mechanism with Bloom filters and a dynamic reputation system for data nonexistence proofs.

Main Results:

• Achieved approximately 15% improvement in query efficiency compared to existing schemes.
• Reduced query path length through segmented filtering and weight optimization.
• Minimized false positives and enhanced query accuracy and reliability via multi-node consensus.

Conclusions:

• The proposed scheme significantly improves on-chain data query performance.
• Multi-node collaborative verification effectively mitigates malicious attack risks and enhances system security.
• The solution is particularly suitable for resource-constrained mobile healthcare applications.