Wrist-worn smart devices are being used to share various sensitive personal information in various fields such as social, medical treatment, sports, etc. Secure pairing establishing a trusted channel between involved devices is a prerequisite to ensure data transmission security. Handshake has been employed to realize secure pairing between devices worn by different users without pre-shared knowledge, the participation of third parties or complicated user interactions. However, existing schemes cannot meet the practical requirement in terms of time delay and security. In this paper, we present a secure and usable pairing scheme utilizing the handshake acceleration data. Specifically, we propose an optimal feature selection algorithm based on Euclidean distance sorting which improves the success rate and security of the system. In addition, we use Pearson correlation coefficient based feature sequence similarity measurement approach to enhance the accuracy of pairing scheme. Theoretical and experimental security analysis indicates that our solution can resist active and passive attacks. What’s more, the experimental results demonstrate that the proposed solution has a high key generation rate of 87 bits per second, and completes security pairing in less than 4 s.

腕戴式智能设备被用于在社会，医疗，体育等各个领域共享各种敏感的个人信息。安全配对在相关设备之间建立可信通道是确保数据传输安全的前提。握手已被用于实现不同用户佩戴的设备之间的安全配对，而无需预先共享知识，第三方的参与或复杂的用户交互。但是，现有方案在时延和安全性上都不能满足实际要求。在本文中，我们提出了一种利用握手加速数据的安全且可用的配对方案。具体来说，我们提出了一种基于欧氏距离排序的最优特征选择算法，可以提高系统的成功率和安全性。此外，我们使用基于Pearson相关系数的特征序列相似度测量方法来提高配对方案的准确性。理论和实验安全性分析表明，我们的解决方案可以抵抗主动和被动攻击。此外，实验结果表明，所提出的解决方案具有每秒87位的高密钥生成速率，并在不到4 s的时间内完成了安全配对。