Privacy‐preserving hands‐free voice authentication leveraging edge technology

IF 1.5 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zaid Sh. Alattar, Tarek Abbes, Faouzi Zerai
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引用次数: 2

Abstract

Although speech recognition technology has improved significantly over the past few years, the absence of reliable voice authentication methods has negatively affected the Internet of Things (IoT). Voice‐activated devices, by design, rely on personal voice biometrics for access‐level security, and insecure voice authentication techniques limit the development of voice interfaces that can deal with multiple users with different privileges. Furthermore, public concerns surrounding biometric systems have been reported. Outsourcing biometric data are liable to various privacy breaches, increasing vulnerability to cyberattacks from both criminal organizations and government agencies. In this article, we address the security and privacy challenges of voice authentication for IoT by presenting an edge‐based, hands‐free, single‐factor authentication scheme that consists of three security‐based features: random phrase verification, anti‐spoof verification, and text‐independent speaker recognition. The use of edge technology allows the integration of this authentication scheme with resource contained IoT devices such as voice assistants. Additionally, privacy is ensured by a novel security protocol that uses chaffing and encryption techniques to protect the users' biometric data stored locally on the edge device. Finally, the scheme does not compromise the user experience as it requires only two short voice inputs.
利用边缘技术保护隐私免手语音认证
虽然语音识别技术在过去几年中有了很大的进步,但缺乏可靠的语音认证方法对物联网(IoT)产生了负面影响。语音激活设备在设计上依赖于个人语音生物识别技术来实现访问级安全,而不安全的语音认证技术限制了能够处理具有不同权限的多个用户的语音接口的发展。此外,公众对生物识别系统的担忧也有报道。外包生物识别数据容易受到各种隐私侵犯,更容易受到犯罪组织和政府机构的网络攻击。在本文中,我们通过提出一种基于边缘的、免提的、单因素身份验证方案来解决物联网语音身份验证的安全和隐私挑战,该方案由三个基于安全的特征组成:随机短语验证、反欺骗验证和与文本无关的说话人识别。边缘技术的使用允许将这种身份验证方案与包含资源的物联网设备(如语音助手)集成。此外,一种新的安全协议确保了隐私,该协议使用干扰和加密技术来保护本地存储在边缘设备上的用户生物特征数据。最后,该方案不会影响用户体验,因为它只需要两个简短的语音输入。
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
自引率
5.30%
发文量
80
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