Integrity protection method for trusted data of IoT nodes based on transfer learning

In order to overcome the problems of high data storage occupancy and long encryption time in traditional integrity protection methods for trusted data of IoT node, this paper proposes an integrity protection method for trusted data of IoT node based on transfer learning. Through the transfer learning algorithm, the data characteristics of the IoT node is obtained, the feature mapping function in the common characteristics of the node data is set to complete the classification of the complete data and incomplete data in the IoT nodes. The data of the IoT nodes is input into the data processing database to verify its security, eliminate the node data with low security, and integrate the security data and the complete data. On this basis, homomorphic encryption algorithm is used to encrypt the trusted data of IoT nodes, and embedded processor is added to the IoT to realize data integrity protection. The experimental results show that: after using the proposed method to protect the integrity of trusted data of IoT nodes, the data storage occupancy rate is only about 3.5%, the shortest time-consuming of trusted data encryption of IoT nodes is about 3 s, and the work efficiency is high.