Enhancing Privacy Preservation of Iot-Based Smart City Using Software-Defined Networking and Differential Privacy Distributions

Internet of Things (IoT) aims at connecting all objects through the Internet. The IoT-based smart city is the concept of employing IoT devices to manage cities more easily, quickly and effectively. Recently, a new paradigm has emerged in the networking world, called software-defined networking (SDN), which separates the control data plane and the data plane. On the other hand, IoT devices embedded in a smart city often detect sensitive data. It is of paramount importance to prevent leaking this data unintentionally, known as privacy preserving. Although there are some methods to preserve data privacy in smart cities, they are either expensive or cannot preserve privacy efficiently. Intending to provide an efficient privacy preserving in a cost-effective smart city, we propose a novel method called differential privacy (DP)-preserving Smart City (DPSmartCity). It maintains the privacy of IoT devices sensitive data by equipping the environment with SDN technologies and leveraging a customized DP technique while its distribution changes frequently. We then mathematically investigate the DP aspect of the given algorithms by providing several proofs. The evaluation results show the effectiveness of the proposed method for diffident parameters, such as overload amount and latency points. Based on our threat model, it is 25.89% more robust against unintentional disclosure of sensitive data from a penetration point of view.