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针对攻击者可能通过某些技术手段如生成式对抗网络(GAN)等窃取深度学习训练数据集中敏感信息的问题,结合差分隐私理论,提出经沃瑟斯坦生成式对抗网络(WGAN)反馈调参的深度学习差分隐私保护的方法.该方法使用随机梯度下降进行优化,设置梯度阈值进行梯度裁剪,对深度学习的优化过程添加噪声实施隐私保护;利用WGAN生成与原始数据相似的最优结果,对比生成结果与原始数据的差异进行反馈调参.实验结果表明,该方法可以有效保护数据集的敏感信息并且具有较好的数据可用性.“,”Aiming at the problem that attackers may steal sensitive information of the deep learning training dataset by some technological means such as the Generative Adversarial Network(GAN) ,combining the differential privacy theory ,the differential privacy protection method was proposed for deep learning based on the Wasserstein generative adversarial network (WGAN) feedback parameter tuning .This privacy protection method is realized by optimization of the stochastic gradient descent , gradient clipping of setting gradient threshold , and noise adding to the optimization process of deep learning;WGAN was used to generate optimized results similar to the original data .The difference of the generated results and the original data were used for feedback parameter tuning .The experiment result shows that this method can effectively protect sensitive private information of the dataset and has preferable data usability .