INTEGRATING PRIVACY PRESERVING IN INTERNET OF THINGS TO ENSURE DATA SECURITY USING VERTICAL PARTITIONING APPROACH

Abstract

Guaranteeing security and data privacy are key issues in the Internet of Things (IoT). For the management of numerous security services, privacy plays a significant role. Devices which capable of being utilized anywhere any place, any device anything, any context anytime, anybody anytime, any business any service, in any network any path is the definition of IoT. Routing for IoT needs to be energy efficient. For multimodal problems, traditional evolutionary algorithms commonly result in population convergence towards a search space’s restricted area, hence, disregarding the local optima’s remainder. The Ant Colony Optimization (ACO) algorithm has garnered much interest and has been researched extensively since it is one of the most efficient techniques for the resolution of optimization problems. This is a population-based heuristic evolution algorithm that is influenced by the results of research of actual ants’ natural collective behaviour. Multimodal optimization’s Evolutionary algorithms can generally identify many optima within a single run and also retain their diversity of population throughout a run. Hence, for multimodal functions, these algorithms have the capability of global optimization. In this work, a hybrid ACO-Evolutionary Multimodal is proposed for energy-efficient routing. The data collected are stored in a vertically partitioned dataset to maintain the privacy of the data.