Asymmetric cryptographic functions based on generative adversarial neural networks for Internet of Things

【Author】 Hao, Xiaohan; Ren, Wei; Xiong, Ruoting; Zhu, Tianqing; Choo, Kim-Kwang Raymond

【Source】FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE

【影响因子】7.307

【Abstract】Increasingly, one should assume that the (digital) environment, e.g., Internet-of-Things (IoT) systems, we operate in is untrusted. In other words, this is a zero trust environment, in the sense that all devices and systems can be compromised and hence, untrusted. However, information sharing in a zero trust environment is more challenging, in comparison to an environment where we can rely on some trusted third-party. To address this challenge, we propose a blockchain-enabled zero trust information sharing protocol that is able to support the filtering of fabricated information and protect participant privacy during information sharing. We then prove the security of our protocol in the universally composable secure framework, and also evaluate its performance using a series of experiments. The evaluation results show that the average execution times of the three key steps in our protocol are 0.059 s, 0.060 s and 0.032 s, which demonstrates its potential for deployment in a real-world setting. (C) 2021 Elsevier B.V. All rights reserved.

【Keywords】Generative adversarial network; Digital signature; Asymmetric encryption; Neural networks; Internet of Things (IoT)

【发表时间】2021 NOV

【收录时间】2022-01-01

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