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Title	Hprop: Hierarchical Privacy-Preserving Route Planning For Smart Cities
ID_Doc	29537
Authors	Tiausas F.; Yasumoto K.; Talusan J.P.; Yamana H.; Yamaguchi H.; Bhattacharjee S.; Dubey A.; Das S.K.
Year	2023
Published	ACM Transactions on Cyber-Physical Systems, 7, 4
DOI	http://dx.doi.org/10.1145/3616874
Abstract	Route Planning Systems (RPS) are a core component of autonomous personal transport systems essential for safe and efficient navigation of dynamic urban environments with the support of edge-based smart city infrastructure, but they also raise concerns about user route privacy in the context of both privately owned and commercial vehicles. Numerous high-profile data breaches in recent years have fortunately motivated research on privacy-preserving RPS, but most of them are rendered impractical by greatly increased communication and processing overhead. We address this by proposing an approach called Hierarchical Privacy-Preserving Route Planning (HPRoP), which divides and distributes the route-planning task across multiple levels and protects locations along the entire route. This is done by combining Inertial Flow partitioning, Private Information Retrieval (PIR), and Edge Computing techniques with our novel route-planning heuristic algorithm. Normalized metrics were also formulated to quantify the privacy of the source/destination points (endpoint location privacy) and the route itself (route privacy). Evaluation on a simulated road network showed that HPRoP reliably produces routes differing only by ≤ 20% in length from optimal shortest paths, with completion times within ∼25 seconds, which is reasonable for a PIR-based approach. On top of this, more than half of the produced routes achieved near-optimal endpoint location privacy (∼1.0) and good route privacy (≥ 0.8). © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Author Keywords	Additional Key Words and PhrasesRoute planning services; location privacy; route-planning algorithms

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