关键词: 小批量随机梯度下降法, 支持向量机, 同态加密, 同态哈希函数, 隐私保护

Abstract: When using a support vector machine (SVM) to process sensitive data, privacy protection is very important. The existing SVM privacypreserving schemes are trained based on batch gradient descent (BGD) algorithm, and they have huge computational overhead. To solve this problem, this paper proposed a privacypreserving scheme for SVM training based on minibatch stochastic gradient descent (Minibatch SGD). Firstly, it designed the SVM training algorithm based on Minibatch SGD. Then, on this basis, it perturbed the model weights by multiplication, used the hardness assumption of integer factorization to ensure the privacy of the model, engaged the homomorphic cryptosystem to encrypt the data, performed SVM training, and then applied the homomorphic hash function for verification. Finally, it constructed the SVM privacypreserving scheme. Against security threats, the paper demonstrated data privacy, model privacy, and model correctness. It carried out simulation experiments and analysis of the scheme. The results show that the proposed scheme can save 92.4% of the computation time on average, while the classification performance is close to the existing schemes.

Key words: Minibatch SGD, SVM, homomorphic encryption, homomorphic hash function, privacypreserving