基于振动的智能手表可用用户认证

Sunwoo Lee, W. Choi, Dong Hoon Lee
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引用次数: 17

摘要

近年来,智能手表得到了广泛的应用,一些提高便利性和用户健康的智能手表应用程序不断被开发和推出。此外,最新的智能手表现在被设计成可以在没有智能手机配对的情况下运行,因此智能手表有必要独立验证用户。在目前的这些设备中,输入个人识别号码(PIN)或模式来验证用户,但这些方法对用户来说交互不方便,安全性不高。与智能手表技术特别相关的是,即使是基于生物特征信息的用户认证也需要能够测量生物特征信息的特殊传感器或用户交互。在本文中,我们提出了一种无需额外设备即可在智能手表上进行用户认证的可用方法。基于每个人的身体结构对振动的吸收、反射和传播都是不同的这一事实,我们的方法被设计为一种挑战-响应方案,其中挑战是当前智能手表中已经内置的多种振动类型的随机序列。对振动的响应由智能手表中的默认陀螺仪和加速度计传感器测量。此外,我们的方法是第一个用于低规格商用智能手表振动和测量响应的工作模型。我们使用商用智能手表对我们的方法进行了评估,结果表明我们的方法能够以1.37%的相等错误率(EER)对用户进行身份验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Usable User Authentication on a Smartwatch using Vibration
Smartwatches have come into wide use in recent years, and a number of smartwatch applications that improve convenience and user health are being developed and introduced constantly. Moreover, the latest smartwatches are now designed to operate without their paired smartphones, and as such, it is necessary for smartwatches to independently authenticate users. In these current devices, personal identification numbers (PIN) or patterns are entered to authenticate users, but these methods require inconvenient interaction for the user and are not highly secure. Particularly relevant to smartwatch technology, even user authentication based on biometric information needs either special sensors capable of measuring biometric information or user interaction. In this paper, we propose a usable method for user authentication on smartwatches without additional devices. Based on the fact that vibration is absorbed, reflected, and propagated differently according to the physical structure of each human body, our method is designed as a challenge-response scheme, in which the challenge is a random sequence of multiple vibration types that are already built into current smartwatches. The responses to vibrations are measured by the default gyroscope and accelerometer sensors in smartwatches. Moreover, our method is the first working model for commercial smartwatch models with low specifications when vibrating and measuring responses. We evaluated our method using a commercial smartwatch, and the results show that our method is able to authenticate a user with an equal error rate (EER) of 1.37%.
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