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.
On Privacy, Security, and Trustworthiness in Distributed Wireless Large AI Models (WLAM)
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；Key Laboratory of Information Processing, Communication and Networking of Zhejiang Province (IPCAN)（浙江省信息处理、通信与网络重点实验室）；National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信重点实验室）；School of Information and Communication Engineering, Beijing University of Posts and Communications（北京邮电大学信息与通信工程学院）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Center for 6G Technology, Khalifa University of Science and Technology（哈利法科技大学6G技术中心）
TinyML NLP Scheme for Semantic Wireless Sentiment Classification with Privacy Preservation
Department of Electrical Engineering and Computer Science, York University（电气工程与计算机科学系，约克大学）；CEMSE Division, King Abdullah University of Science and Technology (KAUST)（CEMSE部门，国王 Abdullah 科学与技术大学（KAUST））
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges
Princeton University（普林斯顿大学）；Imperial College London（帝国理工学院）；The University of Hong Kong（香港大学）；Virginia Tech（弗吉尼亚理工大学）；University of Oulu（奥卢大学）；Ericsson Research（爱立信研究院）
Privacy preserving distributed profile matching in mobile social network
在该文献中，在称为\emph{mobile social network}的特定网络上下文中提出了隐私保护分布式简档匹配协议。这些网络通常部署在或多或少敌对的环境中，需要严格的安全机制。与此同时，由于这些异构网络通常由平板电脑或手机等无线组件构成，因此能源和计算资源有限。这就是为什么本文提出了一种新的加密算法，具有较高的安全性，同时保留资源。该方法是基于椭圆曲线密码学，更具体地说，在一个几乎完全同态密码系统的超奇异椭圆曲线，导致一个安全和有效的保护隐私的分布式配置文件匹配。
