Privacy Preservation for Machine Learning Training and Classification Based on Homomorphic Encryption Schemes

Abstract In recent years, more and more machine learning algorithms depend on the cloud computing. When a machine learning system is trained or classified in the cloud environment, the cloud server obtains data from the user side. Then, the privacy of the data depends on the service provider, it is easy to induce the malicious acquisition and utilization of data. On the other hand, the attackers can detect the statistical characteristics of machine learning data and infer the parameters of machine learning model through reverse attacks. Therefore, it is urgent to design an effective encryption scheme to protect the data’s privacy without breaking the performance of machine learning. In this paper, we propose a novel homomorphic encryption framework over non-abelian rings, and define the homomorphism operations in ciphertexts space. The scheme can achieve one-way security based on the Conjugacy Search Problem. After that, a homomorphic encryption was proposed over a matrix-ring. It supports real numbers encryption based on the homomorphism of 2-order displacement matrix coding function and achieves fast ciphertexts homomorphic comparison without decrypting any ciphetexts operations’ intermediate result. Furthermore, we use the scheme to realize privacy preservation for machine learning training and classification in data ciphertexts environment. The analysis shows that our proposed schemes are efficient for encryption/decryption and homomorphic operations.