HPRoP: Hierarchical Privacy-Preserving Route Planning for Smart Cities

IF 2 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
F. Tiausas, K. Yasumoto, J. P. Talusan, H. Yamana, H. Yamaguchi, Shameek Bhattacharjee, Abhishek Dubey, Sajal K. Das
{"title":"HPRoP: Hierarchical Privacy-Preserving Route Planning for Smart Cities","authors":"F. Tiausas, K. Yasumoto, J. P. Talusan, H. Yamana, H. Yamaguchi, Shameek Bhattacharjee, Abhishek Dubey, Sajal K. Das","doi":"10.1145/3616874","DOIUrl":null,"url":null,"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 \\(\\le 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).","PeriodicalId":7055,"journal":{"name":"ACM Transactions on Cyber-Physical Systems","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Cyber-Physical Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3616874","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 1

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 \(\le 20\% \) 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).
HPRoP:智能城市的分层隐私保护路线规划
路线规划系统(RPS)是自主个人交通系统的核心组成部分,在基于边缘的智能城市基础设施的支持下,对动态城市环境的安全高效导航至关重要,但它们也引发了对私人和商用车用户路线隐私的担忧。幸运的是,近年来发生了许多引人注目的数据泄露事件,促使人们对保护隐私的RPS进行了研究,但由于通信和处理开销的大幅增加,其中大多数都变得不切实际。我们通过提出一种称为分层隐私保护路线规划(HPRoP)的方法来解决这一问题,该方法将路线规划任务划分并分布在多个级别,并保护整个路线上的位置。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新的路线规划启发式算法相结合来实现的。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)和路线本身的隐私(路线隐私)。对模拟道路网络的评估表明,HPRoP可靠地产生的路线与最佳最短路径的长度仅相差\(\le 20\%\),完成时间在~25秒内,这对于基于PIR的方法来说是合理的。除此之外,超过一半的生产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)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
ACM Transactions on Cyber-Physical Systems
ACM Transactions on Cyber-Physical Systems COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
5.70
自引率
4.30%
发文量
40
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信