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在车辆中保护数据:用于动态CAV环境的隐私保护框架
Rahma Hammedi1, David J Brown1, Omprakash Kaiwartya1
1Department of Computer Science, Nottingham Trent University, Nottingham NG11 8NS, UK.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
互联和自动驾驶汽车 (CAV) 产生大量数据,构成隐私风险. 这篇论文探讨了隐私挑战和解决方案,如联邦学习,区块链和SDN,用于安全的智能运输系统.
科学领域:
- 智能运输系统 智能运输系统
- 网络安全 网络安全
- 数据 隐私 数据 隐私 数据
背景情况:
- 连接和自动驾驶汽车 (CAV) 集成先进的自动化和连接.
- 车辆与一切 (V2X) 通信对CAV至关重要,但也引发了隐私问题.
- 动态的CAV环境为保护隐私带来了独特的挑战.
研究的目的:
- 调查CAV系统中不断变化的数据隐私风险.
- 批判性地审查现有的隐私保护方法及其局限性.
- 探索在CAV中保护隐私的支持技术.
主要方法:
- 对隐私保护机制的文献综述.
- 对联合学习,许可区块链和软件定义网络 (SDN) 的分析.
- 在动态车辆环境中评估技术限制.
主要成果:
- 现有的隐私模式与CAV的动态性质作斗争.
- 联合学习,区块链和SDN显示出对隐私保护的承诺.
- 在高流动性环境中确定了当前方法的具体局限性.
结论:
- 针对性建议,以优化CAV中的隐私框架.
- 提高隐私弹性是下一代智能交通的关键.
- 需要可扩展和强大的隐私解决方案,以便广泛采用CAV.
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