A context-aware privacy-preserving method for IoT-based smart city using Software Defined Networking. Issue 87 (November 2019) - Gheisari, Mehdi; Wang, Guojun; Khan, Wazir Zada; Fernández-Campusano, Christian
Abstract: Smart City is an application of the Internet of Things (IoT) with the aim of managing cities without human intervention. Each IoT device that is producing data may sense a sensitive one that should not be disclosed unintentionally. Due to the existence of a large number of devices in the near future, the possibility of information leakage, privacy breach, is increasing. To prevent this, each device applies a privacy-preserving method. We discover that all of the existing solutions have three major drawbacks: (1) applying one static privacy-preserving method for the entire system, (2) sending whole data at once, and (3) not context-aware. These cause unacceptable privacy-preserving degree. To address them, in this paper, at first, we equip IoT-based smart city with Software Defined Networking paradigm (SDN). Then, we mount an efficient privacy-preserving method on top of it that manages flowing data packets of split IoT device' data. We have done extensive simulation through MININET-WIFI to show the effectiveness of our approach. Evaluation results show that our method can be widely applied to the smart city application with a superior performance regarding accuracy, overhead, and penetration rate compared to existing privacy-preserving solutions.