基于相关差异隐私的自动驾驶个性化联合学习方案
Yuan Tian1, Yanfeng Shi1, Yue Zhang1
1School of Computer Engineering, Nanjing Institute of Technology, Nanjing 211167, China.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
这项研究引入了个性化的联合学习方法,用于自动驾驶的相关差异隐私. 它增强了数据隐私和实用性,同时适应了用户差异,为安全的数据共享提供了精致的解决方案.
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:
- 大数据和智能传感器普遍存在,需要安全的数据共享.
- 数据隐私问题很重要,特别是在自动驾驶等应用中.
- 现有的方法经常与用户异质性和数据实用性作斗争.
研究的目的:
- 为自动驾驶提供个性化的联合学习方法.
- 为了加强数据保护,整合相关的差异性隐私.
- 在定制场景中解决传统差异隐私的局限性.
主要方法:
- 联合学习用于每个节点的去中心化模型训练.
- 相关分类分析用于加密高度相关的数据,最大限度地降低系统成本.
- 相关差异隐私应用在共享之前保护数据隐私.
主要成果:
- 拟议的方案提供了根据个人用户需求量身定制的增强隐私.
- 实验结果表明,在处理用户异质性方面,优越的精细化.
- 该方法可以提高数据实用性,而不会影响隐私.
结论:
- 与相关的差异隐私相关的个性化联合学习对自动驾驶有效.
- 这种方法为数据隐私保护提供了更定制和更强大的解决方案.
- 它平衡了数据实用性的需求与严格的隐私要求.
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