Enhancing Data Privacy in Linear Regression Through Homomorphic Encryption

Abstract

Big data analysis is required to predict future trends using machine learning. A lot of processing and computation is required to make this analysis. Conventional encryption methods may help to achieve privacy during the resting stage of data over the cloud. The computation of data needs to be in its original form or needs to be decrypted before any computation, which leaves the data again at risk and makes it more vulnerable. The latest homomorphic encryption algorithms provide a way to perform the computation over data in its encrypted form and help to analyze big data without compromising the privacy and security of data, even when data is in use. In this paper, we implemented the linear regression model over plain data and encrypted data to measure and analyze the fitness, MSE, and RMSE scores of models and the space complexity of plain data and encrypted data. The results show that the fitness score of the linear regression model for plain data and encrypted data is almost the same and differs by only 0.00003551, whereas the RMSE score is 386.9949011 and 364.03753641, respectively. It is observed that the overall performance of the linear regression model improved for encrypted data, and the error is also reduced while maintaining data privacy