Full Program »
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 in several use-cases, e.g., transportation and goods delivery. On the one hand, discovering in advance collisions occurring with other Unmanned Aerial Vehicles (UAVs) in the future could enhance the efficiency of the path planning, further reducing the delivery time and UAV's energy consumption. On the other hand, path-related information such as location and timestamps--key to detecting and avoiding collisions--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 mini-pc, and a mini-drone), and we showed 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, thus emerging as an effective and impactful approach for autonomous UAVs path planning.