Data Privacy Protection Algorithm for Carrier Communication Nodes in Power Internet of Things Based on Frequent Itemsets

Because the original data of the carrier communication node of power Internet of Things is non-stationary and nonlinear, its discreteness is large, which is not conducive to feature extraction, which leads to the possibility of data privacy protection and low data security. Therefore, this paper proposes a data privacy protection algorithm based on frequent itemsets for power Internet of Things carrier communication nodes. Through the set rules, the network resources of the power Internet of Things are analyzed, the corresponding itemsets are connected, the residual components are calculated, and the correlation between two adjacent data is extracted by using frequent itemsets. The comprehensive attributes of the data set are obtained, the local information entropy of the communication node data privacy is described, and the data sequence characteristics of the privacy data are extracted, to construct the data privacy protection objective function and realize the data privacy protection algorithm design of the power Internet of Things carrier communication node. The experimental results show that the algorithm in this paper shows a low data information loss rate on multiple data sets, which can effectively reduce data information loss and protect the privacy and security of data. Under both internal and external attacks, the anti-attack performance is over 98%, after privacy processing by this algorithm, privacy leakage can be limited to the maximum extent, and privacy protection performance is excellent.