Abstract
Benefiting from the computing power and storage power of cloud computing, machine learning tasks usually choose to upload data and models to a third-party server. However, third-party servers may face data privacy leaks in the process of data acquisition, storage, and use, especially in the fields of finance, medical treatment, and biometrics. Homomorphic encryption technology supports ciphertext calculation without decryption, and can be used for machine learning model training and prediction in the ciphertext domain. We use the homomorphic encryption library SEAL to reconstruct the linear regression protocol, use six data sets to conduct experiments, and analyze its performance and security.
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