School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China.
Department of Computer Science, University of Science & Technology Bannu, Bannu, Pakistan.
Math Biosci Eng. 2024 Feb 26;21(3):4165-4186. doi: 10.3934/mbe.2024184.
In recent years, the extensive use of facial recognition technology has raised concerns about data privacy and security for various applications, such as improving security and streamlining attendance systems and smartphone access. In this study, a blockchain-based decentralized facial recognition system (DFRS) that has been designed to overcome the complexities of technology. The DFRS takes a trailblazing approach, focusing on finding a critical balance between the benefits of facial recognition and the protection of individuals' private rights in an era of increasing monitoring. First, the facial traits are segmented into separate clusters which are maintained by the specialized node that maintains the data privacy and security. After that, the data obfuscation is done by using generative adversarial networks. To ensure the security and authenticity of the data, the facial data is encoded and stored in the blockchain. The proposed system achieves significant results on the CelebA dataset, which shows the effectiveness of the proposed approach. The proposed model has demonstrated enhanced efficacy over existing methods, attaining 99.80% accuracy on the dataset. The study's results emphasize the system's efficacy, especially in biometrics and privacy-focused applications, demonstrating outstanding precision and efficiency during its implementation. This research provides a complete and novel solution for secure facial recognition and data security for privacy protection.
近年来，人脸识别技术的广泛应用引发了人们对各种应用的数据隐私和安全的担忧，如提高安全性和简化考勤系统以及智能手机访问。在这项研究中，设计了一种基于区块链的去中心化人脸识别系统（DFRS），旨在克服技术的复杂性。DFRS 开创性地在人脸识别的优势和保护个人隐私权之间找到了一个关键的平衡点，因为在监控越来越多的时代，需要这样做。首先，面部特征被分割成单独的簇，由专门的节点维护，这些节点维护数据隐私和安全。之后，使用生成对抗网络进行数据混淆。为了确保数据的安全性和真实性，面部数据被编码并存储在区块链中。该系统在 CelebA 数据集上取得了显著的结果，这表明了所提出方法的有效性。与现有方法相比，所提出的模型表现出了更高的功效，在数据集上的准确率达到了 99.80%。研究结果强调了该系统的功效，特别是在生物识别和注重隐私的应用中，在实施过程中表现出了出色的精度和效率。本研究为安全人脸识别和数据安全提供了一个完整而新颖的解决方案，用于隐私保护。
