Uncrewed aerial vehicles (UAVs), or drones, are increasingly being used to deliver goods from vendors to customers. In an emerging business model, a drone operator partners with multiple businesses to offer drone delivery as a service. However, this business model poses a privacy risk due to regulations requiring drones to broadcast location information. Third-party observers may leverage broadcast trajectories to link customers to vendors, resulting in potential privacy issues. A probabilistic definition of privacy risk is proposed based on the likelihood that a third-party observer can correctly infer which vendor a customer ordered from. Next, these risks are quantified, and the impacts of order count, drone capacity, decoy stops, and delivery time requirements on privacy are evaluated. Privacy risk mitigation is then formulated as an optimization problem and integrated into the vehicle routing problem. Doing so allows optimization of efficiency while satisfying a privacy constraint. A comparison of the tradeoffs between privacy and efficiency based on the geographical arrangement of vendors and customers is presented. Finally, limitations, including complexity, assumptions, and areas for future work, are discussed.
Ding et al. (Sun,) studied this question.