Design and Performance of Privacy Protection Model for Big Data Transmission Based on Mixed Encryption
DOI:
https://doi.org/10.13052/jcsm2245-1439.1369Keywords:
Big data, symmetric encryption, asymmetric encryption, mixed encryption, privacy protectionAbstract
With the advent of the big data era, data security and privacy protection have become particularly important. Big data has advantages such as large scale and diverse types, but it also brings risks of personal privacy leakage and data abuse. However, the symmetric or asymmetric encryption techniques alone have limitations in big data security and privacy protection. Therefore, a privacy protection model for big data transmission based on mixed encryption is proposed and experimentally validated. The research results indicated that the asymmetric encryption algorithm used had an encryption time of less than 20 ms, and the key space occupation was only 0.031 Kb to 0.063 Kb. After improving the symmetric encryption algorithm, it achieved a lower correlation of 0.16 within 18 ms and increased the number of ciphertext transformations to an average of 82 bits. In the performance verification of mixed encryption technology for 60 MB data packets, the proposed mixed encryption technology took 273.1 ms, the decryption took 254.7 ms, the correlation was as low as 0.12, and the average resistance time in resisting violent attacks exceeded 100 s. The model proposed in the study improves the encryption and decryption speed while ensuring data security, which has important practical application value and theoretical significance for data privacy protection in big data environments.
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Widiasanti I, Zanuara A A F, Maulidina D F, Atmaja F A R, Putri S R. Implementation of Big Data in the Zoom and Google Classroom Applications as Online Learning Media. Indonesian Journal of Education and Mathematical Science, 2023, 4(2): 86–92.
Srihith I D, Donald A D, Srinivas T A S, Thippanna G, Anjali D. Locking down big data: a comprehensive survey of data encryption methods. International Journal of Advanced Research in Science, Communication, and Technology, 2023, 3(2): 84–93.
Guan S, Zhang C, Wang Y, Liu, W. Hadoop-based secure storage solution for big data in cloud computing environment. Digital Communications and Networks, 2024, 10(1): 227–236.
Saravanan S, Poornima N. Big Data analytics for privacy through ND-homomorphic encryption. Journal of Control and Decision, 2023, 10(1): 64–71.
Chelladurai S P, Rajagopalan T. Intelligent Digital Envelope for Distributed Cloud-Based Big Data Security. Comput. Syst. Sci. Eng., 2023, 46(1): 951–960.
Ajala O A, Arinze C A, Ofodile O C, Okoye C C, Daraojimba O D. Reviewing advancements in privacy-enhancing technologies for big data analytics in an era of increased surveillance. World Journal of Advanced Engineering Technology and Sciences, 2024, 11(1): 294–300.
Lo’ai A T, Saldamli G. Reconsidering big data security and privacy in cloud and mobile cloud systems. Journal of King Saud University-Computer and Information Sciences, 2021, 33(7): 810–819.
Awaysheh F M, Aladwan M N, Alazab M, Alawadi S, Cabaleiro J C, Pena T F. Security by design for big data frameworks over cloud computing. IEEE Transactions on Engineering Management, 2021, 69(6): 3676–3693.
Hu X, Jiang R, Shi M, Shang J. A privacy protection model for health care big data based on trust evaluation access control in cloud service environment. Journal of Intelligent & Fuzzy Systems, 2020, 38(3): 3167–3178.
Huang L, Zhou J, Lin J, Deng S. View analysis of personal information leakage and privacy protection in big data era—based on Q method. Aslib Journal of Information Management, 2022, 74(5): 901–927.
Marqas R B, Almufti S M, Ihsan R R. Comparing Symmetric and Asymmetric cryptography in message encryption and decryption by using AES and RSA algorithms. Xi’an Jianzhu Keji Daxue Xuebao/ Journal of Xi’an University of Architecture & Technology, 2020, 12(3): 3110–3116.
Wang S, Chen H, Zhao Q, Q. Y., Guo L Y, Deng X Y, Si W G, Sun Z Q. Preserving scheme for user’s confidential information in smart grid based on digital watermark and asymmetric encryption. Journal of Central South University, 2022, 29(2): 726–740.
Son L D, An T V, Thuy N N. Improving the asymmetric encryption algorithm based on genetic algorithm, application in online information transmission. International Journal of Electronic Security and Digital Forensics, 2021, 13(6): 612–629.
Ghayvat H, Pandya S, Bhattacharya P, Zuhair M, Rashid M, Hakak S, Dev K. CP-BDHCA: Blockchain-based Confidentiality-Privacy preserving Big Data scheme for healthcare clouds and applications. IEEE Journal of Biomedical and Health Informatics, 2021, 26(5): 1937–1948.
Atadoga A, Farayola O A, Ayinla B S, Amoo O O, Abrahams T O. A comparative review of data encryption methods in the USA and Europe. Computer Science & IT Research Journal, 2024, 5(2): 447–460.
Hebbi C, Mamatha H. Comprehensive Dataset Building and Recognition of Isolated Handwritten Kannada Characters Using Machine Learning Models. Artificial Intelligence and Applications, 2023, 1(3): 179–190.
Widiasanti I, Puteri A A A, Savitri D, Anjani D, Nathania N. Big Data in E-Learning: Review Study on Google Classroom Usage and Big Data Challenges. Scripta: English Department Journal, 2023, 10(1): 121–127.
Fan Y, Zhang W, Bai J, Lei X, Li K. Privacy-preserving deep learning on big data in cloud. China Communications, 2023, 20(11): 176–186.
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