基于特征编码和对称同型加密的高效隐私保护面部识别.
Limengnan Zhou1, Qinshi Li2, Hui Zhu3
1School of Electronic and Information Engineering, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528402, China.
Entropy (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了使用面部特征编码方法 (FFCM) 和同型加密来保护隐私的面部识别系统. 这种新的方法通过减少计算来提高效率和安全性,以更好地保护面部隐私.
科学领域:
- 计算机科学 计算机科学
- 密码学 密码学 密码学 密码学
- 生物识别信息 生物识别信息
背景情况:
- 保护隐私的面部识别系统面临着计算低效率和高的挑战.
- 现有的方法往往难以平衡安全性,效率和隐私保护.
研究的目的:
- 提出一种有效且安全的保护隐私的面部识别方法.
- 为了减少计算和提高系统效率,同时保护面部数据.
主要方法:
- 使用了面部特征编码方法 (FFCM) 和对称同态加密.
- 使用基于神经网络的FFCM构建了一个N-ary特征树,以加速匹配.
- 采用轻量级对称同态加密,用于在密码文本中安全的共弦相似度计算.
主要成果:
- 在密码文本搜索效率方面取得了显著的改进.
- 与最先进的解决方案相比,表情认证效率提高了4%至6%.
- 安全分析证实了对被动和主动攻击的弹性.
结论:
- 拟议的方法为保护隐私的面部识别提供了一种高效,安全和意识到的方法.
- 这种技术在大规模应用中显示出了显著的改进.
- 面部隐私得到了有效的保护,而不会影响系统性能.
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