Redundant data detection and deletion to meet privacy protection requirements in blockchain-based edge computing environment

With the rapid development of information technology, IoT devices play a huge role in physiological health data detection. The exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge nodes. The storage capacity of edge nodes close to users is limited. We should store hotspot data in edge nodes as much as possible, so as to ensure response timeliness and access hit rate; However, the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data; How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging problem. Our paper proposes a redundant data detection method that meets the privacy protection requirements. By scanning the cipher text, it is determined whether each sub-message of the data in the edge node meets the requirements of the hot data. It has the same effect as zero-knowledge proof, and it will not reveal the privacy of users. In addition, for redundant sub-data that does not meet the requirements of hot data, our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the data. We use Content Extraction Signature (CES) to generate the remaining hot data signature after the redundant data is deleted. The feasibility of the scheme is proved through safety analysis and efficiency analysis.