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Asymmetric cryptographic functions based on generative adversarial neural networks for Internet of Things
Future Generation Computer Systems ( IF 7.5 ) Pub Date : 2021-06-08 , DOI: 10.1016/j.future.2021.05.030
Xiaohan Hao , Wei Ren , Ruoting Xiong , Tianqing Zhu , Kim-Kwang Raymond Choo

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.



中文翻译:

基于生成对抗神经网络的物联网非对称密码函数

人们越来越应该假设我们运营的(数字)环境,例如物联网 (IoT) 系统是不受信任的。换句话说,这是一个零信任环境,因为所有设备和系统都可能受到损害,因此不受信任。然而,与我们可以依赖某些受信任第三方的环境相比,零信任环境中的信息共享更具挑战性。为了应对这一挑战,我们提出了一种支持区块链的零信任信息共享协议,该协议能够支持过滤虚假信息并在信息共享过程中保护参与者的隐私。然后我们在通用可组合安全框架中证明我们的协议的安全性,并使用一系列实验评估其性能。

更新日期:2021-06-13
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