TY - JOUR
T1 - Leakage-Resilient Authenticated Key Exchange for Edge Artificial Intelligence
AU - Zhang, Jie
AU - Zhang, Futai
AU - Huang, Xin
AU - Liu, Xin
N1 - Publisher Copyright:
© 2004-2012 IEEE.
PY - 2021
Y1 - 2021
N2 - Edge Artificial Intelligence (AI) is a timely complement of cloud-based AI. By introducing intelligence to the edge, it alleviates privacy concerns of streaming and storing data to the cloud, enables real-time operations where milliseconds matter, and brings AI services to remote areas with poor networking infrastructures. Security is a significant problem in Edge AI applications such as self-driving cars and intelligent healthcare. Since the edge devices are empowered to process data and take actions, attacking and compromising them can cause serious damage. However, the wide deployment of computationally limited devices in edge environments and the increasing happening of side-channel (or leakage) attacks pose critical challenges to security. This article thereby aims to enhance the security for Edge AI by designing and developing lightweight and leakage-resilient authenticated key exchange (LRAKE) protocols. Compared with available LRAKE protocols, the proposed protocols in this article can be effortless applied in some mainstreaming security and communication standards. Moreover, this article realizes prototypes and presents implementation details; and a use case of applying the proposed protocol in Bluetooth 5.0 is illustrated. The theoretical design and implementation details will provide a guidance of applying the LRAKE protocols in Edge AI applications.
AB - Edge Artificial Intelligence (AI) is a timely complement of cloud-based AI. By introducing intelligence to the edge, it alleviates privacy concerns of streaming and storing data to the cloud, enables real-time operations where milliseconds matter, and brings AI services to remote areas with poor networking infrastructures. Security is a significant problem in Edge AI applications such as self-driving cars and intelligent healthcare. Since the edge devices are empowered to process data and take actions, attacking and compromising them can cause serious damage. However, the wide deployment of computationally limited devices in edge environments and the increasing happening of side-channel (or leakage) attacks pose critical challenges to security. This article thereby aims to enhance the security for Edge AI by designing and developing lightweight and leakage-resilient authenticated key exchange (LRAKE) protocols. Compared with available LRAKE protocols, the proposed protocols in this article can be effortless applied in some mainstreaming security and communication standards. Moreover, this article realizes prototypes and presents implementation details; and a use case of applying the proposed protocol in Bluetooth 5.0 is illustrated. The theoretical design and implementation details will provide a guidance of applying the LRAKE protocols in Edge AI applications.
KW - Edge AI
KW - Leakage-resilience
KW - edge computing
KW - key exchange
KW - side-channel attacks
UR - http://www.scopus.com/inward/record.url?scp=85119469947&partnerID=8YFLogxK
U2 - 10.1109/TDSC.2020.2967703
DO - 10.1109/TDSC.2020.2967703
M3 - Article
AN - SCOPUS:85119469947
SN - 1545-5971
VL - 18
SP - 2835
EP - 2847
JO - IEEE Transactions on Dependable and Secure Computing
JF - IEEE Transactions on Dependable and Secure Computing
IS - 6
ER -