Design and Performance of Privacy Protection Model for Big Data Transmission Based on Mixed Encryption
Zhiqiang Chen, Zhihua Song, Tao Zhang, Yong Xiang Wei · Journal of Cyber Security and Mobility · 2024
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.