Research Article
Simulation of Privacy and Security Features of Big Data Based on Data De-Redundancy Technology
@INPROCEEDINGS{10.4108/eai.19-5-2023.2334436, author={Fang Liu}, title={Simulation of Privacy and Security Features of Big Data Based on Data De-Redundancy Technology}, proceedings={Proceedings of the 2nd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2023, May 19--21, 2023, Hangzhou, China}, publisher={EAI}, proceedings_a={ICBBEM}, year={2023}, month={7}, keywords={privacy big data; redundant data; secure shared access; attribute encryption simulation}, doi={10.4108/eai.19-5-2023.2334436} }- Fang Liu
Year: 2023
Simulation of Privacy and Security Features of Big Data Based on Data De-Redundancy Technology
ICBBEM
EAI
DOI: 10.4108/eai.19-5-2023.2334436
Abstract
This project takes big data as the research object and data redundancy as the entry point to study the new privacy encryption technology for big data. In this project, Bloom filtering method is used to reduce the dimension of massive data, hash function is used to process the redundancy algorithm, and bit selection is carried out on the bit sequence to optimize the number of extension functions. In addition, based on the existing analysis method based on extensible key, the optimal ciphertext strategy is adopted to realize the security sharing of cloud data more efficiently, reduce the storage scale of data units in attribute space, and reduce the number of shared parameters, so as to ensure the security of cloud data. Experiments show that this method can effectively intercept the attacked information, enhance the security of big data, and ensure the validity of the password, which is a good and feasible method [1].