Secure Framework for Future Smart City

Hamza Djigal, Jun Feng, Jiamin Lu
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引用次数: 10

Abstract

With the recent advancements in the information and communication technologies, large number of devices are connecting to the Internet, hence large volumes of data in different formats and from different sources are generating. Consequently, on one hand dynamic and heterogeneous data sharing and management, in the ecosystem of Internet of Things (IoT), where every smart object is connected to Internet, presents new research challenges. On the other hand, citizen privacy preserving is another challenge, because he/she has to send his/her information to a service provider, to obtain the required information. This information is sensitive since it can reveal information about an individual. An attacker or a malicious service provider can utilize this sensitive information for their own business or something else. This paper presents a Secure Framework for Future Smart City (SEFSCITY), for better city living and governance, based on Cloud Computing IoT and Distributed Computing. We first present the architecture of SEFSCITY, which is based on Multi-Cloud and Cloud Federation approach; then we propose a security protocol for our framework. In our security model, we use Zero-Knowledge Protocol based on Elliptic Curve Discrete Logarithm Problem. Finally, we validate our architecture by conducting several scenarios that we have implemented using Cloud Analyst tool. The results show that in all scenarios, the cost infrastructure remains the same for the cloud customer, and our approach is benefic for the cloud provider in term of revenues and data processing time
未来智慧城市的安全框架
随着资讯及通讯科技的发展,大量的设备连接到互联网,因此产生了大量不同格式和来源的数据。因此,一方面,在万物互联的物联网生态系统中,动态、异构的数据共享与管理提出了新的研究挑战。另一方面,公民隐私保护是另一个挑战,因为他/她必须将自己的信息发送给服务提供商,以获得所需的信息。此信息很敏感,因为它可能会泄露有关个人的信息。攻击者或恶意服务提供者可以将这些敏感信息用于自己的业务或其他目的。本文提出了一个基于云计算物联网和分布式计算的未来智慧城市安全框架(SEFSCITY),以改善城市生活和治理。首先介绍了基于多云和云联合方法的安全安全体系结构;然后,我们为我们的框架提出了一个安全协议。在我们的安全模型中,我们使用基于椭圆曲线离散对数问题的零知识协议。最后,我们通过执行使用Cloud Analyst工具实现的几个场景来验证我们的体系结构。结果表明,在所有情况下,云客户的成本基础设施保持不变,我们的方法在收入和数据处理时间方面有利于云提供商
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