Research on Application of Neural Network Model in Location Privacy Protection of Wireless Sensor Networks

Abstract

Due to the characteristics of WSN (Wireless sensor network), attackers can easily eavesdrop on data packets transmitted between single or even multiple communication links, and separate sensitive data from multiple sensor information, which poses a great threat to location privacy. Therefore, it is very important to effectively protect the privacy of training sample data while using WSN. Neural network is an important research hotspot in AI field, and it is a model close to biological neural network in machine learning algorithm. In this paper, an application of neural network model in WSN location privacy protection is given. A CNN (Convolutional Neural Network) location privacy protection prediction protocol based solely on additive homomorphisms is proposed, which can effectively ensure that input features, model parameters, and intermediate values are not leaked during the prediction process. The experimental results show that the proposed method has good robustness and can effectively protect the private location of the source node.