2024/02/06 by Manish Bansal, Bansal, Manish, Pramsu Srivastava +2 · 1 citation
Computer Science · #FOS: Computer and information sciences #Information Theory (cs.IT) #Multi-Agent Systems and Negotiation #Networking and Internet Architecture (cs.NI) #Opportunistic and Delay-Tolerant Networks #Privacy-Preserving Technologies in Data
paper · pdf · doi:10.48550/arxiv.2402.03702
openalex publication_date 2024/02/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In Vehicle-to-Everything networks that involve multi-hop communication, the Road Side Units (RSUs) typically aim to collect location information from the participating vehicles to provide security and network diagnostics features. While the vehicles commonly use the Global Positioning System (GPS) for navigation, they may refrain from sharing their precise GPS coordinates with the RSUs due to privacy concerns. Therefore, to jointly address the high localization requirements by the RSUs as well as the vehicles' privacy, we present a novel spatial-provenance framework wherein each vehicle uses Bloom filters to embed their partial location information when forwarding the packets. In this framework, the RSUs and the vehicles agree upon fragmenting the coverage area into several smaller regions so that the vehicles can embed the identity of their regions through Bloom filters. Given the probabilistic nature of Bloom filters, we derive an analytical expression on the error-rates in provenance recovery and then pose an optimization problem to choose the underlying parameters. With the help of extensive simulation results, we show that our method offers near-optimal Bloom filter parameters in learning spatial provenance. Some interesting trade-offs between the communication-overhead, spatial privacy of the vehicles and the error rates in provenance recovery are also discussed.