An Edge Computing-enhanced Internet of Things Framework for Privacy-preserving in Smart City

To supervise massive generated data by the Internet of Things (IoT) efficiently, we face two issues that should be addressed which are: (1) heterogeneity or satisfying diversity among IoT devices, and (2) privacy-preserving or preventing unintentional disclosure of sensitive data. Through observation, we found that existing solutions apply one common privacy-preserving rule for all devices while they address the heterogeneity issue separately that lead to unappealing performance. In this paper, we propose a framework for addressing the heterogeneity issue and privacy-preserving of IoT devices at the network edge using a novel proposed ontology data model. Besides, it leverages the proposed ontology to obtain a privacy-preserving method by frequently changing the privacy-preserving behaviors of loT devices. Through simulation, we show that our solution overhead is less than 9 percent in the worst situation so that it is affordable to most loT devices in one of its applications that is smart city. (C) 2019 Elsevier Ltd. All rights reserved.