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Secure Framework for Future Smart City

机译:安全框架未来智能城市

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摘要

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.
机译:随着最近的信息和通信技术的进步,大量设备正在连接到互联网,因此不同格式和来自不同源的大量数据正在生成。因此,在一方面动态和异构的数据共享和管理,在物联网(IOT)的生态系统中,每个智能对象都连接到互联网,呈现出新的研究挑战。另一方面,公民隐私保留是另一个挑战,因为他/她必须向服务提供商发送他/她的信息,以获取所需信息。此信息很敏感,因为它可以透露有关个人的信息。攻击者或恶意服务提供商可以为自己的业务或其他东西利用这种敏感信息。本文基于云计算物联网和分布式计算,为未来的智能城市(SEFSCity)提供了一个安全的智能城市(SEFSCITY)的安全框架,以实现更好的城市生活和治理。我们首先介绍SEFSCity的架构,基于多云和云联合方法;然后我们为我们的框架提出了一种安全协议。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,我们通过使用云分析师工具进行多种方案来验证我们的架构。结果表明,在所有方案中,云客户的成本基础设施仍然是相同的,我们的方法在收入和数据处理时间期间对云提供商有益。

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