Data Privacy Protection Model Based on Homomorphic Encryption and Newton Iterative Method
In order to solve the problem of serious privacy leakage faced by machine learning,this paper proposes a Logistic regression model of big data privacy protection based on homomorphic encryption.The model uses Logistic regression algorithm to train and predict encrypted data,and the whole process will not reveal any privacy.The training data is encrypted by using Paillier homomorphic encryption algorithm,and the logistic regression model suitable for ciphertext data set is established by using Newton iterative algorithm.The algorithm can be used as a privacy preserving technique to construct binary classification model,and can be applied to various problems that can be modeled by logistic regression.This paper implements the algorithm on MNIST and dermatology data sets respectively,evaluates the model after further decrypting the plaintext,and then calculates the accuracy of the model.Finally,comparing the model of this paper with the conclusions of relevant literatures,it can be seen that the model of this paper has high accuracy,which shows the feasibility of the model in practical application.
big dataregression modelprivacy protectionhomomorphic encryption
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