A smart privacy preserving framework for industrial IoT using hybrid meta-heuristic algorithm

Abstract

Abstract Industrial Internet of Things (IIoT) seeks more attention in attaining enormous opportunities in the field of Industry 4.0. But there exist severe challenges related to data privacy and security when processing the automatic and practical data collection and monitoring over industrial applications in IIoT. Traditional user authentication strategies in IIoT are affected by single factor authentication, which leads to poor adaptability along with the increasing users count and different user categories. For addressing such issue, this paper aims to implement the privacy preservation model in IIoT using the advancements of artificial intelligent techniques. The two major stages of the designed system are the sanitization and restoration of IIoT data. Data sanitization hides the sensitive information in IIoT for preventing it from leakage of information. Moreover, the designed sanitization procedure performs the optimal key generation by a new Grasshopper–Black Hole Optimization (G–BHO) algorithm. A multi-objective function involving the parameters like degree of modification, hiding rate, correlation coefficient between the actual data and restored data, and information preservation rate was derived and utilized for generating optimal key. The simulation result establishes the dominance of the proposed model over other state-of the-art models in terms of various performance metrics. In respect of privacy preservation, the proposed G–BHO algorithm has achieved 1%, 15.2%, 12.6%, and 1% enhanced result than JA, GWO, GOA, and BHO, respectively.