An approach for data integrity authentication and protection in fog computing

IF 0.6 Q4 COMPUTER SCIENCE, THEORY & METHODS Multiagent and Grid Systems Pub Date : 2022-08-30 DOI:10.3233/mgs-220210
M.N. Babitha, M. Siddappa
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Abstract

The data integrity verification process in cloud has become more promising research area in several Internet of Things (IoT) applications. The traditional data verification approaches use encryption in order to preserve data. Moreover, fog computing is considered as extensively employed virtualized platform and it affords various services including storage as well as services interconnected to computing and networking between user and data center based on standard cloud computing. Moreover, fog computing is an extensive description of cloud computing. Thus, fog servers effectively decrease the latency by integrating fog servers. In this paper, novel model for data integrity authentication and protection is designed in IoT cloud-fog model. This method mainly comprises fog nodes, cloud server, IoT nodes, and key distribution center. Here, dynamic and secure key is produced based on the request to key distribution center based on hashing, Exclusive OR (XOR), homomorphic encryption and polynomial. The fog nodes are employed to encrypt the data gathered from IoT nodes as well as allocate the nearby nodes based on Artificial Bee Colony-based Fuzzy-C-Means (ABC FCM) – based partitioning approach. The proposed data integrity authentication approach in IoT fog cloud system outperformed than other existing methods with respect to detection rate, computational time and memory usage of 0.8541, 34.25 s, and 54.8 MB, respectively.
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雾计算中数据完整性认证与保护方法
云环境下的数据完整性验证过程已成为物联网应用中较有前景的研究领域。传统的数据验证方法使用加密来保存数据。雾计算被认为是一种被广泛应用的虚拟化平台,它提供包括存储在内的各种服务,以及基于标准云计算的用户与数据中心之间的计算互联和网络服务。此外,雾计算是云计算的广泛描述。因此,雾服务器通过集成雾服务器有效地减少了延迟。本文在物联网云雾模型中设计了一种新的数据完整性认证与保护模型。该方法主要由雾节点、云服务器、物联网节点和密钥分发中心组成。基于哈希、异或(XOR)、同态加密和多项式,根据对密钥分发中心的请求生成动态安全密钥。雾节点用于对从物联网节点收集的数据进行加密,并基于基于人工蜂群的模糊c均值(ABC FCM)划分方法对附近节点进行分配。本文提出的物联网雾云系统数据完整性认证方法在检测率、计算时间和内存占用方面均优于现有方法,分别为0.8541、34.25 s和54.8 MB。
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来源期刊
Multiagent and Grid Systems
Multiagent and Grid Systems COMPUTER SCIENCE, THEORY & METHODS-
CiteScore
1.50
自引率
0.00%
发文量
13
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