Secure lightweight data using scheme in 5G industrial Internet systems
To address the privacy and security challenges of data analysis in the 5G industrial Internet environ-ment,a lightweight solution based on privacy computing and logistic regression techniques is proposed.Priva-cy computing techniques are utilized to protect sensitive data.Data encryption and digital signatures are used to ensure the privacy and integrity of data transmission,achieving the goal of not disclosing original training data and model parameters.Lightweight cryptographic algorithms are employed to realize the function of privacy computing,so as to adapt to the training and tuning with a large amount of data.The results show that the pre-diction accuracy of the proposed model is 98.62％.It has small computational and communication costs.Lo-gistic regression model training can be performed by data users without accessing the raw data.It can ensure both data availability and invisibility and prevent exposure of the model parameters,which is suitable for low-power devices in 5G industrial Internet environment.
industrial Internetdata using securityprivacy computinglogistic regression
万方数据
维普
