Aiming at the privacy leakage problem in the data mining model and the opacity of existing privacy protection technologies， a more universal image differential privacy-generative adversarial network （IDP-GAN） combining differential privacy with the image generation model—generative adversarial network （GAN） is proposed. IDP-GAN uses the Laplace implementation mechanism to reasonably allocate Laplace noise to the input features of the affine transformation layer and the polynomial approximation coefficients of the loss function of the output layer. While achieving differential privacy protection， IDP-GAN effectively reduces the consumption of privacy budget during training. Experiments on the standard data sets MNIST and CelebA verify that IDP-GAN can generate higher quality image data. In addition， membership inference attacks experiments prove that IDP-GAN has better ability to resist attacks.