Toward a Privacy-Preserving Face Recognition System: A Survey of Leakages and Solutions

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Lamyanba Laishram, Muhammad Shaheryar, Jong Taek Lee, Soon Ki Jung
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Abstract Recent advancements in face recognition (FR) technology in surveillance systems make it possible to monitor a person as they move around. FR gathers a lot of information depending on the quantity and data sources. The most severe privacy concern with FR technology is its use to identify people in real-time public monitoring applications or via an aggregation of datasets without their consent. Due to the importance of private data leakage in the FR environment, academia and business have given it a lot of attention, leading to the creation of several research initiatives meant to solve the corresponding challenges. As a result, this study aims to look at privacy-preserving face recognition (PPFR) methods. We propose a detailed and systematic study of the PPFR based on our suggested six-level framework. Along with all the levels, more emphasis is given to the processing of face images as it is more crucial for FR technology. We explore the privacy leakage issues and offer an up-to-date and thorough summary of current research trends in the FR system from six perspectives. We also encourage additional research initiatives in this promising area for further investigation.

实现保护隐私的人脸识别系统:泄漏与解决方案调查
摘要 监控系统中人脸识别(FR)技术的最新进展使监视一个人的行动成为可能。根据数量和数据来源的不同,人脸识别技术可以收集大量信息。人脸识别技术最令人担忧的隐私问题是,它在实时公共监控应用中或在未经本人同意的情况下通过数据集的汇总来识别人的身份。由于私人数据泄漏在 FR 环境中的重要性,学术界和企业界对此给予了极大关注,并发起了多项旨在解决相应挑战的研究计划。因此,本研究旨在探讨保护隐私的人脸识别(PPFR)方法。我们根据建议的六级框架对 PPFR 进行了详细而系统的研究。在所有层次中,我们更加重视人脸图像的处理,因为这对于人脸识别技术来说更为关键。我们探讨了隐私泄露问题,并从六个方面对当前 FR 系统的研究趋势进行了最新、最全面的总结。我们还鼓励在这一前景广阔的领域开展更多的研究活动,以作进一步探讨。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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