Leakage-Resilient Authenticated Key Exchange for Edge Artificial Intelligence

Jie Zhang, Futai Zhang*, Xin Huang, Xin Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2835-2847
Number of pages13
JournalIEEE Transactions on Dependable and Secure Computing
Issue number6
Publication statusPublished - 2021


  • Edge AI
  • Leakage-resilience
  • edge computing
  • key exchange
  • side-channel attacks


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