Efficient privacy-preserving authentication framework for edge-assisted Internet of Drones

In recent years, small-scale unmanned aerial vehicles (UAVs), or drones, have demonstrated their promising capabilities in supporting a range of applications, such as surveillance, inspection, package delivery, precision agriculture, etc. Given an increasing number of UAVs in the low-altitude airspace, connecting UAVs to form the Internet of Drones (IoDs) is a promising trend to enhance the flying safety as well as the service quality of UAVs. However, due to the open-access communication environment, IoD also raises security and privacy concerns, including authentication threat as well as the leakage of identity, location, and flying routes. In order to address the security and privacy concerns in IoD, this paper proposes an efficient privacy-preserving authentication framework. By utilizing the lightweight online/offline signature design, our framework assures the authentication efficiency when deploying on resource-constrained small-scale UAVs. Considering the high mobility of UAVs, a predictive authentication approach is investigated with mobile edge computing (MEC) in our framework to further reduce the authentication cost for potential authentication activities. Meanwhile, by designing a buffer pseudonym and public key update strategy, our framework enables the protection of privacy in terms of UAVs' identity, location, and flying routes. Thorough analysis is carried out to demonstrate the fulfillment and feasibility of our framework in terms of security, privacy, and efficiency. (C) 2019 Elsevier Ltd. All rights reserved.