基于属性的大数据安全自适应同态加密。

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Big Data Pub Date : 2024-10-01 Epub Date: 2021-12-13 DOI:10.1089/big.2021.0176
R Thenmozhi, S Shridevi, Sachi Nandan Mohanty, Vicente García-Díaz, Deepak Gupta, Prayag Tiwari, Mohammad Shorfuzzaman
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引用次数: 0

摘要

由于移动电话的普及,全球互联网使用量急剧增加。这种极高的互联网使用率产生了海量数据,换句话说就是大数据。安全和隐私是大数据管理中需要考虑的主要问题。因此,本文开发了基于属性的自适应同态加密(AAHE)来增强大数据的安全性。在所提出的方法中,引入了基于对立函数的黑寡妇优化(OBWO),以按照 AAHE 方法选择最佳密钥参数。通过考虑对立函数,加强了黑寡妇优化(BWO)的收敛性分析。所提出的方法有不同的流程,即流程设置、加密和解密流程。研究人员用非阿贝尔环和密码文本格式中的同构过程对所提出的方法进行了评估。此外,该方法还用于提高与共轭检验问题相关的单向安全性。之后,开发了同态加密技术来保护大数据的安全。研究考虑了两种类型的大数据,如成人数据集和匿名微软网络数据集,以验证所提出的方法。在加密时间、解密时间、密钥大小、处理时间、下载和上传时间等性能指标的帮助下,对所提出的方法进行了评估,并与 Rivest-Shamir-Adleman (RSA)和椭圆曲线加密法(ECC)等传统加密技术进行了比较。此外,还将密钥生成过程与 BWO、粒子群优化(PSO)和萤火虫算法(FA)等传统方法进行了比较。结果表明,所提出的方法比其他方法更优越,可在不久的将来实时应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Attribute-Based Adaptive Homomorphic Encryption for Big Data Security.

There is a drastic increase in Internet usage across the globe, thanks to mobile phone penetration. This extreme Internet usage generates huge volumes of data, in other terms, big data. Security and privacy are the main issues to be considered in big data management. Hence, in this article, Attribute-based Adaptive Homomorphic Encryption (AAHE) is developed to enhance the security of big data. In the proposed methodology, Oppositional Based Black Widow Optimization (OBWO) is introduced to select the optimal key parameters by following the AAHE method. By considering oppositional function, Black Widow Optimization (BWO) convergence analysis was enhanced. The proposed methodology has different processes, namely, process setup, encryption, and decryption processes. The researcher evaluated the proposed methodology with non-abelian rings and the homomorphism process in ciphertext format. Further, it is also utilized in improving one-way security related to the conjugacy examination issue. Afterward, homomorphic encryption is developed to secure the big data. The study considered two types of big data such as adult datasets and anonymous Microsoft web datasets to validate the proposed methodology. With the help of performance metrics such as encryption time, decryption time, key size, processing time, downloading, and uploading time, the proposed method was evaluated and compared against conventional cryptography techniques such as Rivest-Shamir-Adleman (RSA) and Elliptic Curve Cryptography (ECC). Further, the key generation process was also compared against conventional methods such as BWO, Particle Swarm Optimization (PSO), and Firefly Algorithm (FA). The results established that the proposed method is supreme than the compared methods and can be applied in real time in near future.

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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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