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走向强大且增强隐私的面部识别:采用GAN和深度学习的分散式区块链式方法
Muhammad Ahmad Nawaz Ul Ghani1, Kun She1, Muhammad Arslan Rauf1
1School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China.
Mathematical biosciences and engineering : MBE
|March 29, 2024
概括
本研究介绍了一种基于区块链的新型去中心化面部识别系统 (DFRS),以增强数据隐私和安全性. 该DFRS达到99.80%的准确性,平衡面部识别的好处与个人隐私保护.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 生物识别信息 生物识别信息
背景情况:
- 面部识别技术被广泛使用,引发了大量的数据隐私和安全问题.
- 现有的系统努力平衡面部识别的实用性与保护个人隐私权.
- 增加监控需要先进的安全和私人生物识别解决方案.
研究的目的:
- 开发一个基于区块链的去中心化面部识别系统 (DFRS),优先考虑数据隐私和安全.
- 通过创建安全数据处理的新方法来解决面部识别的复杂性.
- 建立面部识别优势与保护个人隐私之间的关键平衡.
主要方法:
- 面部特征被细分成由专业节点管理的集群,确保数据隐私和安全.
- 生成对抗网络 (GAN) 用于数据模糊.
- 面部数据被编码并存储在区块链上,以保证安全性和真实性.
主要成果:
- 拟议的DFRS在CelebA数据集上实现了99.80%的高精度.
- 与现有的面部识别方法相比,该系统的效率更高.
- 实施展示了生物识别和专注于隐私的应用程序的卓越精度和效率.
结论:
- 基于区块链的去中心化面部识别系统为安全的面部识别提供了一种新而全面的解决方案.
- 在日益增长的监控时代,DFRS有效地提高了数据安全性,并保护了个人隐私.
- 这项研究为保护隐私的生物识别应用提供了一个强大的框架.
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