HPRoP: Hierarchical Privacy-preserving Route Planning for Smart Cities
HPRoP: Hierarchical Privacy-preserving Route Planning for Smart Cities
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DOI：
10.1145/3616874
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发表时间：
2023-08
影响因子：
2.3
通讯作者：
F. Tiausas;K. Yasumoto;J. P. Talusan;H. Yamana;Hirozumi Yamaguchi;Shameek Bhattacharjee;Abhishek Dubey;Sajal K. Das
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文献类型：
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作者：
F. Tiausas;K. Yasumoto;J. P. Talusan;H. Yamana;Hirozumi Yamaguchi;Shameek Bhattacharjee;Abhishek Dubey;Sajal K. Das
路线规划系统（RPS）是自动个人交通系统的核心组成部分，对于在基于边缘的智能城市基础设施的支持下安全有效地导航动态城市环境至关重要，但它们也引起了人们对私人和商用车辆用户路线隐私的担忧。幸运的是，近年来许多备受瞩目的数据泄露事件激发了对隐私保护RPS的研究，但由于通信和处理开销大大增加，其中大多数都变得不切实际。我们解决这个问题，提出了一种方法，称为分层隐私保护路线规划（HPRoP），它划分和分布在多个级别的路线规划任务，并保护位置沿着整个路线。这是通过将惯性流划分，私人信息检索（PIR）和边缘计算技术与我们的新的路线规划启发式算法相结合来完成的。还制定了标准化指标，以量化源/目的地点的隐私（端点位置隐私）和路由本身（路由隐私）。对模拟道路网络的评估表明，HPRoP可靠地生成与最优最短路径长度仅相差≤ 20%的路线，完成时间在25秒内，这对于基于PIR的方法是合理的。除此之外，超过一半的生成路线实现了接近最佳的端点位置隐私（≥ 1.0）和良好的路线隐私（≥ 0.8）。
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).