生物识别中的隐私增强技术概览

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Pietro Melzi, Christian Rathgeb, Ruben Tolosana, Ruben Vera, Christoph Busch
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引用次数: 0

摘要

隐私增强技术是实施基本数据保护原则的技术。在生物识别方面,已经引入了不同类型的隐私增强技术来保护存储的生物识别数据,这些数据通常被归类为敏感数据。在这方面,已经提出了各种分类法和概念分类,并开展了标准化活动。不过,这些工作主要针对隐私增强技术的某些子类别,因此缺乏普遍性。这项工作在一个统一的框架内概述了用于生物识别的隐私增强技术的概念。在每个处理步骤中都详细强调了现有概念的关键特性和差异。讨论了现有技术的基本特征和局限性,并将其与数据保护技术和原则联系起来。此外,还介绍了用于生物识别的隐私增强技术的评估方案和方法。本文旨在作为生物识别应用数据保护领域的切入点,面向有经验的研究人员和非专业人员。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Overview of Privacy-Enhancing Technologies in Biometric Recognition

Privacy-enhancing technologies are technologies that implement fundamental data protection principles. With respect to biometric recognition, different types of privacy-enhancing technologies have been introduced for protecting stored biometric data which are generally classified as sensitive. In this regard, various taxonomies and conceptual categorizations have been proposed and standardisation activities have been carried out. However, these efforts have mainly been devoted to certain sub-categories of privacy-enhancing technologies and therefore lack generalization. This work provides an overview of concepts of privacy-enhancing technologies for biometric recognition in a unified framework. Key properties and differences between existing concepts are highlighted in detail at each processing step. Fundamental characteristics and limitations of existing technologies are discussed and related to data protection techniques and principles. Moreover, scenarios and methods for the assessment of privacy-enhancing technologies for biometric recognition are presented. This paper is meant as a point of entry to the field of data protection for biometric recognition applications and is directed towards experienced researchers as well as non-experts.

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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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