An efficient privacy-preserving blockchain storage method for internet of things environment

Area of Science:

• Computer Science
• Information Technology
• Cybersecurity

Background:

• Decentralized trust management is crucial, with sharding-based blockchains proposed for resource-constrained Internet of Things (IoT).
• Machine learning enhances query efficiency in sharded blockchains by classifying and locally storing hot data.
• Existing methods face privacy challenges as block features used for learning are exposed.

Purpose of the Study:

• To propose an efficient and privacy-preserving blockchain storage method for IoT environments.
• To address the privacy concerns associated with using block features in machine learning models for sharded blockchains.
• To improve data query speed and protect user privacy in IoT blockchain applications.

Main Methods:

• A novel privacy-preserving blockchain storage method utilizing federated extreme learning machine for hot block classification.
• Integration of the proposed method with ElasticChain, a sharded blockchain model, for efficient hot block storage.
• Definition of five comprehensive features for evaluating hot blocks: objective feature, historical popularity, potential popularity, storage requirements, and training value.

Main Results:

• The proposed method effectively protects user privacy by preventing other nodes from accessing hot block features.
• Local storage of hot blocks significantly improves data query speed within the IoT environment.
• Experimental results on synthetic data validate the accuracy and efficiency of the proposed blockchain storage model.

Conclusions:

• The developed method offers an efficient solution for privacy-preserving blockchain storage in IoT.
• Federated learning is a viable approach to enhance security and privacy in sharded blockchain systems.
• The model demonstrates strong performance in terms of accuracy, efficiency, and privacy protection for IoT data management.