Nowadays, face recognition technology has been dramatically boosted by the advances in deep learning and big data fields. However, this also poses grand challenges in protecting personal identity information in intelligent applications of the Internet of Things (IoT). Existing methods based on the KK -Same algorithm have low effectiveness for protecting personal identity while preserving face attributes. In this article, we propose an attribute-preserving face de-identification framework called Enhanced Embedded AutoEncoders to address this problem. Our framework consists of three parts: 1) a privacy removal network (PRN); 2) a feature selection network; and 3) a privacy evaluation network. The main purpose of our framework is to ensure that the PRN is capable of discarding information involving identity privacy and retaining desired face attributes for certain prediction applications. In order to achieve this goal, the design of the PRN is crucial. Specifically, we employ two different autoencoders, one of which is embedded within the other. Extensive experimental results show that our framework outperforms existing methods by an average of 3.42%–26.22% in terms of data utility under comparable face de-identification performance, which indicates that the proposed framework can not only effectively retain face attributes but also protect personal identity well.

中文翻译：

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增强型嵌入式自动编码器：保留属性的人脸去识别框架


如今，深度学习和大数据领域的进步极大地推动了人脸识别技术的发展。然而，这也对物联网智能应用中的个人身份信息保护提出了巨大挑战。现有的基于KK-Same算法的方法在保护个人身份的同时保留人脸属性的有效性较低。在本文中，我们提出了一种称为增强型嵌入式自动编码器的保留属性的人脸去识别框架来解决这个问题。我们的框架由三部分组成：1）隐私删除网络（PRN）； 2）特征选择网络； 3）隐私评估网络。我们框架的主要目的是确保 PRN 能够丢弃涉及身份隐私的信息并保留某些预测应用所需的人脸属性。为了实现这一目标，PRN的设计至关重要。具体来说，我们采用两种不同的自动编码器，其中一个嵌入在另一个中。大量的实验结果表明，在可比的人脸去识别性能下，我们的框架在数据效用方面平均优于现有方法3.42%–26.22%，这表明所提出的框架不仅可以有效保留人脸属性，而且可以保护个人身份出色地。