Anonymous Lightweight Authenticated Key Agreement Protocol for Fog-Assisted Healthcare IoT System

被引:5
|
作者
Qiao, Hui [1 ]
Dong, Xuewen [1 ]
Jiang, Qi [2 ]
Ma, Siqi [3 ]
Liu, Chao [4 ]
Xi, Ning [2 ]
Shen, Yulong [1 ]
机构
[1] Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Shaanxi, Peoples R China
[2] Xidian Univ, Sch Cyber Engn, Xian 710071, Shaanxi, Peoples R China
[3] Univ New South Wales, Sch Engn & Informat Technol, Sydney, NSW 2052, Australia
[4] Univ Maryland Baltimore, Dept Comp Sci & Elect Engn, Baltimore, MD 21250 USA
基金
中国国家自然科学基金;
关键词
Anonymity; fog nodes; healthcare-Internet of Things (H-IoT); key agreement protocol; lightweight; MUTUAL AUTHENTICATION; ACCESS-CONTROL; EFFICIENT; PRIVACY; SCHEME; SECURITY;
D O I
10.1109/JIOT.2023.3270300
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The impact of fog-assisted healthcare Internet of Things (H-IoT) system is immense. The smart H-IoT equipments can upload healthcare information to fog nodes with low latency and high mobility. To facilitate secure interactions among three parties, including smart H-IoT equipments, fog nodes, and a cloud server, over the public and insecure channels, a few authenticated key agreement (AKA) protocols are proposed. However, existing works are constructed based on expensive cryptographic primitives (e.g., bilinear pairing), which lead to high computation costs. Besides, the anonymity of H-IoT users is failed to be provided. To tackle these issues, an anonymous and lightweight three-party AKA protocol (ALAKAP) is proposed, which leverages an efficient cryptographic primitive (i.e., Chebyshev chaotic map operation) to generate a shared session key among three parties and achieve security (anonymity and other six properties) and efficiency simultaneously. It then formally proves the security of ALAKAP under the broadly accepted Burrows-Abadi-Needham (BAN) logic model and demonstrates how the proposed protocol satisfies the desired requirements in the fog-assisted H-IoT system. Finally, the performance of ALAKAP is validated by conducting the experiments on Amazon EC2 and Raspberry Pi. The results show that our work can achieve at least 44% higher improvement than the state-of-the-art works.
引用
收藏
页码:16715 / 16726
页数:12
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