arXiv:2409.17287cs.CRcs.LG2024-09

用区块链+变分信息瓶颈,让车联网更安全省数据。

Blockchain-Enabled Variational Information Bottleneck for Data Extraction Based on Mutual Information in Internet of Vehicles

  • 分离编码解码网络降低计算负担
  • 实验表明BVIB显著减少数据交互量
  • 适合关注车联网隐私与效率的开发者

车联网(IoV)可缓解单个车辆算力不足和数据处理能力弱的问题,但也带来用户隐私泄露风险。引入区块链技术可建立安全的数据传输链路,解决车辆算力不足及网络传输安全性问题。然而随着IoV发展,车辆间及车辆与基站、路边单元间的数据交互量持续增加,亟需进一步减少交互规模,智能数据压缩成为关键。变分信息瓶颈(VIB)技术有助于训练编码解码模型,大幅降低需传输的数据量。本文提出一种融合区块链与VIB的新方法(BVIB),旨在减轻计算负载并增强网络安全性。首先通过分离编码与解码网络构建新架构以缓解计算压力,随后设计新算法提升IoV网络安全性能。同时分析数据提取率对系统延迟的影响,确定最优提取率。基于Python与C++搭建实验框架,全面仿真验证显示,BVIB在各项指标上均优于现有基础方法。

原文摘要 · Abstract (English)

The Internet of Vehicles (IoV) network can address the issue of limited computing resources and data processing capabilities of individual vehicles, but it also brings the risk of privacy leakage to vehicle users. Applying blockchain technology can establish secure data links within the IoV, solving the problems of insufficient computing resources for each vehicle and the security of data transmission over the network. However, with the development of the IoV, the amount of data interaction between multiple vehicles and between vehicles and base stations, roadside units, etc., is continuously increasing. There is a need to further reduce the interaction volume, and intelligent data compression is key to solving this problem. The VIB technique facilitates the training of encoding and decoding models, substantially diminishing the volume of data that needs to be transmitted. This paper introduces an innovative approach that integrates blockchain with VIB, referred to as BVIB, designed to lighten computational workloads and reinforce the security of the network. We first construct a new network framework by separating the encoding and decoding networks to address the computational burden issue, and then propose a new algorithm to enhance the security of IoV networks. We also discuss the impact of the data extraction rate on system latency to determine the most suitable data extraction rate. An experimental framework combining Python and C++ has been established to substantiate the efficacy of our BVIB approach. Comprehensive simulation studies indicate that the BVIB consistently excels in comparison to alternative foundational methodologies.

车联网区块链信息瓶颈数据压缩

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