Location Privacy Protection for UAVs in Package Delivery and IoT Data Collection

Unmanned aerial vehicles (UAVs) are well-known for violating citizens privacy either inadvertently or deliberately. However, UAVs could be victims of privacy violations themselves in the sense that an adversary observing a UAV can infer its destination. This paper proposes several privacy-preserving mechanisms (PPMs) for protecting a UAV’s location privacy. In particular, we address the privacy protection problem in two major UAV applications that require significantly different measures: (i) package delivery, and (ii) Internet of Things (IoT) data collection. In the package delivery application, we propose two different PPMs to randomize the UAV’s trajectory such that the observing adversary is confused about the UAV’s destination; we provide privacy guarantees and analyze the trade-off with energy consumption. In the IoT data collection scenario, the UAV is not necessarily required to hover exactly above the IoT device; hence, we propose a different PPM according to which the UAV chooses a random spot around the IoT device for data collection. Then, considering a minimum mean squared error (MMSE) criterion, we obtain the privacy leakage to the adversary. We also analyze the mean peak age of information (PAoI) of the network and show that the proposed method does not degrade the mean PAoI significantly. Finally, considering the limitations of the MMSE approach for some applications, we also develop a differential privacy (DP)-based counterpart for this PPM. We observe that the mean PAoI degrades significantly in Laplacian DP but is acceptable in Gaussian DP.