一个有条件的隐私保护身份认证方案,用于汽车互联网中的联合学习
1Institute of Information Security, Beijing Electronic Science and Technology Institute, Beijing 100070, China.
Entropy (Basel, Switzerland)
|July 26, 2024
概括
本研究介绍了CPPA-SM2,这是一个用于在联网汽车中保护隐私的联合学习的新方案. 它允许匿名参与模型培训,提高汽车互联网 (IoV) 的安全性和效率.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 互联汽车利用联合学习 (FL) 进行增强服务,由人工智能和物联网的进步驱动.
- 车辆互联网 (IoV) 环境面临安全漏洞,在FL模型培训期间危及数据完整性和用户隐私.
- 现有的隐私解决方案,如差异隐私和加密,在数据可用性和计算复杂性方面存在权衡.
研究的目的:
- 为联网汽车的联合学习提出一个有条件的保护隐私的身份身份验证方案 (CPPA-SM2).
- 通过在IoV.内匿名参与FL培训来实现有效的隐私保护.
- 加强联合学习模型的安全性和完整性,以防止动态车辆网络中的潜在攻击.
主要方法:
- 开发CPPA-SM2方案,整合有条件的隐私保护和身份认证.
- 实施车辆在联合学习过程中的匿名参与机制.
- 通过实验结果进行绩效评估,将CPPA-SM2与现有的最先进方案进行比较.
主要成果:
- 与当前的方法相比,CPPA-SM2显著降低了与签名,验证和通信相关的开销.
- 拟议的方案提供了增强的安全功能,同时保持有效的隐私保护为联合学习.
- 实验验证证证实了CPPA-SM2在汽车互联网环境中的有效性和效率.
结论:
- CPPA-SM2 提供了一个强大的解决方案,用于在联网汽车中保护隐私的联合学习.
- 该计划有效平衡安全,隐私和效率,解决现有方法的局限性.
- 在IOV生态系统中,CPPA-SM2促进了AI模型的安全和匿名协作培训.
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