Accelerometer-Based Key Generation and Distribution Method for Wearable IoT Devices
Accelerometer-Based Key Generation and Distribution Method for Wearable IoT Devices
复制标题
基于加速度计的可穿戴物联网设备密钥生成和分发方法
DOI：
10.1109/jiot.2020.3014646
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发表时间：
2021
影响因子：
10.6
通讯作者：
Ye Li
中科院分区：
文献类型：
--
作者：
Fangmin Sun;Weilin Zang;Huang Haohua;Ildar Farkhatdinov;Ye Li
随着可穿戴物联网设备的快速发展，其应用变得越来越普遍，从社交网络、支付、导航到健康和活动监控。这些设备之间的通信安全对于保护传输的敏感信息免遭篡改和窃听至关重要。随着加速度计集成到可穿戴物联网设备中，基于步态的生物识别加密技术已成为可穿戴设备的数据保护工具。本文提出了一种轻量级的基于噪声的组密钥生成方法，该方法利用施加在原始加速度信号上的噪声信号来生成具有高随机性和比特生成率的M比特密钥。此外，设计了一种基于签名滑动窗口编码（SSWC）的共同特征提取方法，以提取共同特征，以便在不同身体部位佩戴的设备之间共享生成的M位密钥。最后，使用公共数据集实现并评估了基于模糊保险库的组密钥分发系统。对所提出的密钥生成和分发方法进行的综合分析证明，通过引入的基于噪声的过程生成的二进制密钥具有高熵，并且可以高效地通过 NIST 和 Dieharder 统计测试。实验结果证明了所提出的基于SSWC的共同特征提取方法分别在类内和类间特征的相似性和可区分性方面的鲁棒性。
With the fast development of wearable IoT devices, their applications are becoming more and more pervasive, ranging from social networking, payment, and navigation to health and activity monitoring. The security of the communication between these devices is essential to protect the transmitted sensitive information from tampering and eavesdropping. With the integration of accelerometers into wearable IoT devices, the gait-based biometric cryptography technology has emerged as a data securing tool for wearables. This article proposes a lightweight noise-based group key generation method, which utilizes the noise signals imposed on the raw acceleration signals to generate an M-bit key with high randomness and bit generation rate. Moreover, a signed sliding window coding (SSWC)-based common feature extraction method was designed to extract the common feature for sharing the generated M-bit key among devices worn on different body parts. Finally, a fuzzy vault-based group key distribution system was implemented and evaluated using a public data set. The performed comprehensive analysis of the proposed key generation and distribution method proved that the binary keys generated via the introduced noise-based procedure have high entropy and can pass both the NIST and Dieharder statistical tests with high efficiency. The experimental results obtained prove the robustness of the proposed SSWC-based common feature extraction method in terms of the similarity and discriminability of intra- and inter-class features, respectively.