Privacy-Preserving Trajectory Matching on Autonomous Unmanned Aerial Vehicles

Autonomous Unmanned Aerial Vehicles (UAVs) are increasingly deployed nowadays, thanks to the additional features and enhanced flexibility they provide, e.g., for transportation and goods delivery. On the one hand, discovering in advance collisions occurring with other UAVs in the future could enhance the efficiency of the path planning, reducing further the delivery time and UAVs' energy consumption. On the other hand, location and timestamps-key to detecting and avoiding collisions in advance-are sensitive and cannot be shared indiscriminately with untrusted entities. This paper solves the aforementioned challenging problem by proposing PPTM, a new protocol for efficient and effective privacy-preserving trajectory matching on autonomous UAVs. PPTM allows two UAVs, possibly not connected to the Internet, to discover any spatial and temporal collisions in their future paths, without revealing to the other party anything else than the colliding time and coordinates. To this aim, PPTM grounds on a dedicated tree-based algorithm, namely, Incremental Capsule Matching, tailored to the unique features of spatio-temporal data, and it also integrates a lightweight privacy-preserving proximity testing solution for performing private comparisons. We tested our solution on real devices with heterogeneous processing capabilities (a regular laptop, a tiny processing unit, and a mini-drone), showing that PPTM can perform privacy-preserving trajectory matching even in a few milliseconds, up to 98.27% quicker compared to the most efficient competing solution.