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.

翻译：在涉及多跳通信的车联万物网络中，路侧单元通常旨在收集参与车辆的位置信息以提供安全与网络诊断功能。尽管车辆普遍使用全球定位系统进行导航，但出于隐私考虑，它们可能拒绝向路侧单元共享精确的GPS坐标。因此，为同时满足路侧单元的高定位需求与车辆的隐私保护，我们提出了一种新颖的空间溯源框架。在该框架中，每辆车辆在转发数据包时使用布隆过滤器嵌入其部分位置信息。具体而言，路侧单元与车辆协商将覆盖区域划分为若干较小区域，使车辆能够通过布隆过滤器嵌入其所在区域的标识。基于布隆过滤器的概率特性，我们推导了溯源恢复误差率的解析表达式，进而构建优化问题以选择底层参数。通过大量仿真结果，我们证明该方法在空间溯源学习中能够提供接近最优的布隆过滤器参数。此外，本文还讨论了通信开销、车辆空间隐私与溯源恢复误差率之间的若干有趣权衡。