Effective privacy preserving model based on adversarial CNN with IBOA in the social IoT systems for CEC

Abstract

SummaryIn the present scenario, the social Internet of Things (IoT) is one of the emerging technologies in combination with collaborative edge computing (CEC). The CEC solves the issue of storage and computing with the help of deep learning models that make full usage of the edge‐computing abilities. The robustness of the deep learning models is ineffective, and the edge devices in the CEC are threatened by the malicious attacks. Therefore, a new data protection framework is proposed in this manuscript to avoid the security crisis and privacy leakage of CEC. A new adversarial sample generation model is introduced in this manuscript on the basis of intensive butterfly optimization algorithm (IBOA) that effectively reduces the time complexity of the framework to linear. Here, IBOA is chosen because an extra intensive exploitation step is included that guides the proposed framework to converge quickly towards global optimum and to avoid local optima trap. Then, the adversarial training concept is incorporated with the convolutional neural network (CNN) model for sentence similarity analysis. On the other hand, the anti‐dropout layers are combined with the adversarial CNN model to reduce the overfitting concern. The proposed framework, adversarial CNN with IBOA, obtained higher results with f1‐score of 88.70%, accuracy of 89.40%, correct rate of 88.95%, recall of 89.48%, and precision of 89.41% on the Microsoft Research Paraphrase Corpus (MSRP) dataset. The obtained results are superiorly better than the conventional deep learning models.