Journal of Software:2018.29(S2):96-107

(南京航空航天大学 计算机科学与技术学院, 江苏 南京 211106)
Leakage-Resilient Password Entry on Smartwatches Based on Semantic Tactile Feedback Guide
WANG Peng-Cheng,YANG Qiu-Long,TU Hua-Wei
(College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)
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Received:June 15, 2018    
> 中文摘要: 当今社会智能手表的使用越来越广泛,其中存储了用户大量的个人信息,需要设计合适的方法对其进行保护.PIN是密码是使用广泛的一种方式,但存在抗泄露性不足的问题.提出了一种智能手表身份认证方案,基于传统的数字密码认证设计,通过震动语义提示输入的密码位数.开展了3个实验来研究这种方法的表现.首先研究了能否被用户快速并且准备判别的震动时长组合.结果显示400ms和100ms的组合使用效果最好.随后设计了一组震动提示方案,并建立了震动和密码第几位的映射关系,经由实验证实了该方案能够被有效地记忆与实践.最后测试了在模拟真实情况下的密码输入.结果表明,设置5位密码进行4位输入可以导致保证较快的输入速度和较高的准确度,同时,能够保证较高的密码抗泄露性.为智能手表的身份认证设计提供了新的思路.
中文关键词: 智能手表  身份认证  震动  安全  穿戴设备
Abstract:Nowadays, smartwatches are increasingly used in our daily lives. Smartwatches store a large number of personal information of users and it is necessary to design appropriate ways to protect them. PIN is a widely adopted method, but it is not resistant to shoulder-surfing. This work proposes a smart-watch-based identity authentication scheme. This scheme is based on the traditional PIN authentication and prompt password entry by vibration. Three experiments have been designed to examine the performance of this method. In the first experiment, it is tested that what kind of vibration time combination is more acceptable. Results show that the vibration combination of 400 ms and 100 ms is the optimal one. In the second experiment, a set of vibration prompt scheme is designed to establish the mapping relationship between vibration and number. Results prove that the scheme can be effectively remembered and practiced. In the last experiment, the actual password input process is simulated and the traditional unlock method is compared with. Results show that inputting four digits of five-digit password can lead to an overall fast entry speed and high accuracy, while maintaining a high security. This study offers insights into identification design for smartwatches.
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基金项目:国家重点研发计划(2017YFB0802300);国家自然科学基金(61602236);江苏省自然科学基金(BK20160801);中国博士后科学基金(2016M591843);江苏省博士后科学基金(1501053B) 国家重点研发计划(2017YFB0802300);国家自然科学基金(61602236);江苏省自然科学基金(BK20160801);中国博士后科学基金(2016M591843);江苏省博士后科学基金(1501053B)
Foundation items:National Key Research and Development Program of China (2017YFB0802300); National Natural Science Foundation of China (61602236); Natural Science Foundation of Jiangsu Province (BK20160801); China Postdoctoral Science Foundation (2016M591843); Jiangsu Postdoctoral Science Foundation (1501053B)
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WANG Peng-Cheng,YANG Qiu-Long,TU Hua-Wei.Leakage-Resilient Password Entry on Smartwatches Based on Semantic Tactile Feedback Guide.Journal of Software,2018,29(S2):96-107