The rapid development of machine learning makes itself one of the most effective tools in the data mining research community.However

the training of algorithm often needs a large amount of user data

which brings a great risk of privacy leakage to users.Due to the complex statistical characteristics and semantic richness of the data

traditional private data publishing methods usually sanitize original data too excessively to lead to low data availability and uselessness in data mining tasks.In this paper

a differential private data publishing method based on generative adversarial network (GAN) is proposed.The differential privacy of the GAN model is realized by adding carefully designed noise to the gradients during the training procedure

so that the GAN can generate unlimited synthetic data conforming to the original statistical characteristics without disclosing any privacy.Aiming at the problems of low quality synthetic data and slow convergence in the existing similar methods

several optimization strategies are designed to adjust the privacy budget allocation and reduce the overall noise scale.Moreover

we provide rigorous proof that the synthetic data satisfies the differential privacy.Comparisons with existing methods on public datasets show that the method proposed can generate private data with higher quality more efficiently

which is suitable for various data analysis tasks.