欧洲航空局:保护隐私的环境基于传感器的身份验证
Andraž Krašovec1,2, Gianmarco Baldini1, Veljko Pejović2
1Joint Research Centre, European Commission, Via Enrico Fermi 2749, 21027 Ispra, Italy.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究介绍了P2ESA,一种使用对抗深度学习的方法,用于在身份验证期间保护物联网 (IoT) 设备的敏感用户数据. 它确保了高度的识别准确性,同时完全掩盖了私人信息.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 物联网 (IoT) 设备在日常生活中越来越普遍.
- 物联网数据可以用于用户识别和身份验证.
- 收集的数据可能无意中揭示出超出认证需求的敏感个人信息.
研究的目的:
- 开发一种方法来从物联网数据中删除隐私泄露信息.
- 在隐私增强后保持高用户身份验证性能.
- 解决机器学习管道中边缘设备的计算限制.
主要方法:
- 利用对抗性的深度学习技术来隐私信息模糊.
- 开发了用于计算弱的边缘设备的卸载技术.
- 在多式联网物联网数据集上实施和培训P2ESA系统.
主要成果:
- 实现了用户识别率在75.85%和93.31%之间,明显超过了6.67%的基线.
- 证明了P2ESA系统的高效部署和培训.
- 成功掩盖了敏感的用户信息,同时保留了身份验证实用程序.
结论:
- 在物联网环境中,P2ESA为安全认证提供了一个有前途的解决方案.
- 该方法有效地平衡了用户识别准确性和强大的隐私保护.
- P2ESA解决了机器学习应用中的边缘设备的计算挑战.
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