哈尔滨商业大学学报（自然科学版）2024，Vol.40Issue(2)：186-192,7.
基于动态学习率边界的隐私保护算法
Privacy protection algorithm based on dynamic learning rate boundary
摘要
Abstract
Deep learning optimization algorithms were prone to privacy leakage when training on data,and convolutional neural networks incurred a huge memory overhead when performing privacy calculations due to the calculation of the gradient of each sample.To address the above problems,a dynamic learning rate bounding algorithm for differential privacy combined with hybrid re-shading clipping was proposed.Combining the AdaBound optimization algorithm with differential privacy alleviated the extreme learning rate and instability of the algorithm during training,and reduced the impact on the model convergence speed due to the addition of noise during backpropagation.The use of hybrid re-shading clipping on the convolutional layer simplified the overhead cost of direct computation of gradient in the update,which could effectively train the differential privacy models.Simulation experiments were conducted to compare with other classical differential privacy algorithms,which showed that the algorithm achieved higher accuracy under the same privacy budget,with better performance and better privacy protection for the model.关键词
差分隐私/深度学习/随机梯度下降/图像分类/自适应算法/学习率剪裁Key words
differential privacy/deep learning/stochastic gradient descent/image classification/adaptive algorithms/learning rate clipping分类
信息技术与安全科学引用本文复制引用
钱振..基于动态学习率边界的隐私保护算法[J].哈尔滨商业大学学报（自然科学版）,2024,40(2):186-192,7.基金项目
安徽省科技带头人及后备人选(No.2019h211) （No.2019h211）
