基于梯度方向筛选的差分隐私文本合成OA

Deep learning models enhance performance by memorizing training data,but this also poses a risk of training data leakage.Differential privacy,as a mainstream privacy protection method,effectively mitigates this risk.However,existing differentially private data synthesis approaches suffer from slow model convergence and low data usability.To address these issues,we propose the TVDPSGD-LM_D framework.This approach introduces TVDPSGD,a threshold-validated differentially private optimization algorithm that incorporates a validation mechanism to filter gradient directions during differentially private model training.By discarding ineffective updates,this approach accelerates model convergence.TVDPSGD-LM embeds TVDPSGD into a language generation model to synthesize labeled text datasets that maintain statistical similarity to the original data.Additionally,a pretrained classifier is used to filter the generated text,removing samples where the classification results do not match the assigned labels,thereby improving the quality of the synthetic dataset.Experimental results on public datasets demonstrate that the proposed method preserves data privacy while achieving a classification accuracy of 89.4％on the processed synthetic dataset.