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

Blockchain is a key technology to realize decentralized trust management. In recent studies, sharding-based blockchain models are proposed and applied to the resource-constrained Internet of Things (IoT) scenario, and machine learning-based models are presented to improve the query efficiency of the sharding-based blockchains by classifying hot data and storing them locally. However, in some scenarios, these presented blockchain models cannot be deployed because the block features used as input in the learning method are privacy. In this paper, we propose an efficient privacy-preserving blockchain storage method for the IoT environment. The new method classifies hot blocks based on the federated extreme learning machine method and saves the hot blocks through one of the sharded blockchain models called ElasticChain. The features of hot blocks will not be read by other nodes in this method, and user privacy is effectively protected. Meanwhile, hot blocks are saved locally, and data query speed is improved. Furthermore, in order to comprehensively evaluate a hot block, five features of hot blocks are defined, including objective feature, historical popularity, potential popularity, storage requirements and training value. Finally, the experimental results on synthetic data demonstrate the accuracy and efficiency of the proposed blockchain storage model.