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Research of Key Technologies of Big Data Security based on Fog Computing

机译:基于雾计算的大数据安全关键技术研究

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In the context of the integration of Internet of Things and big data, the influx of large amounts of data has increased the burden on cloud computing centers. In particular, the security of big data has become more and more prominent. In order to solve these problems, the characteristics and advantages of fog computing are utilized. With the wide application of the Internet of Things in various industries, fog computing, as an extension of cloud computing, play a more and more obvious role. As a rising star, it has obvious advantages in data security protection. Storage is an important part of fog computing and improving its storage efficiency and confidentiality is equivalent to improving the security of edge data. For big data, its structure optimization, data cleaning, data standardization and other technologies are proposed and widely used in edge storage of fog computing. These main technologies improve the confidentiality of big data, and the optimal storage value replacement algorithm and ant colony algorithm are the improvement of big data storage efficiency to speed up data backup and conversion, and prevent the catastrophic consequences of data corruption and attacks. At the end of the paper, an example analysis will ensure the reliability of the technical application.
机译:在物联网和大数据集成的背景下,大量数据的涌入增加了云计算中心的负担。特别是大数据的安全性越来越突出。为了解决这些问题,利用了雾计算的特征和优点。随着物联网在各个行业的广泛应用,雾计算作为云计算的扩展,起着越来越明显的作用。作为后起之秀,它在数据安全保护方面具有明显的优势。存储是雾计算的重要组成部分,提高其存储效率和机密性等同于提高边缘数据的安全性。对于大数据,提出了其结构优化,数据清理,数据标准化等技术,并广泛应用于雾计算的边缘存储。这些主要技术提高了大数据的机密性,最优的存储值替换算法和蚁群算法是大数据存储效率的提高,可以加快数据的备份和转换,防止数据损坏和攻击的灾难性后果。在本文的最后,将通过示例分析来确保技术应用程序的可靠性。

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