Department of Computer Science and Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India.
Centre of Advanced Data Science, Vellore Institute of Technology, Vellore, Chennai, India.
Big Data. 2024 Oct;12(5):343-356. doi: 10.1089/big.2021.0176. Epub 2021 Dec 13.
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.
由于移动电话的普及，全球互联网的使用急剧增加。这种极端的互联网使用产生了大量的数据，换句话说，大数据。安全性和隐私性是大数据管理中需要考虑的主要问题。因此，在本文中，开发了基于属性的自适应同态加密（AAHE）来提高大数据的安全性。在提出的方法中，引入了基于对立的黑寡妇优化（OBWO）通过遵循 AAHE 方法选择最佳的密钥参数。通过考虑对立函数，增强了黑寡妇优化（BWO）的收敛分析。所提出的方法有不同的过程，即过程设置、加密和解密过程。研究人员用非交换环和密文格式中的同态过程评估了所提出的方法。此外，它还用于提高与共轭检验问题相关的单向安全性。然后，同态加密用于保护大数据。该研究考虑了两种类型的大数据，即成人数据集和匿名 Microsoft 网络数据集，以验证所提出的方法。借助加密时间、解密时间、密钥大小、处理时间、下载和上传时间等性能指标，对所提出的方法进行了评估，并与传统加密技术（如 Rivest-Shamir-Adleman（RSA）和椭圆曲线加密（ECC））进行了比较。此外，还将密钥生成过程与传统方法（如 BWO、粒子群优化（PSO）和萤火虫算法（FA））进行了比较。结果表明，所提出的方法优于比较方法，并可在不久的将来应用于实时应用。
