Based on generative adversary networks and the differential privacy mechanism, a differentially private sequence generative adversarial net (DP-SeqGAN) was proposed, Kids Wand with which the privacy of text sequence data sets can be filtered out.DP-SeqGAN can be used to automatically extract important features of a data set and then generate a new data set which was close to the original one in terms of data distributions.Based on differential privacy, randomness is introduced to the model, which improves the privacy of the generated data set and further reduces the over fitting of the discriminator.The proposed DP-SeqGAN was universal, so there is no need to adjust the model adaptively for datasets or design L-Tyrosine complex masking rules against dataset characters.The experiments show that the privacy and usability of a sequence data set are both improved significantly after it is processed by the DP-SeqGAN model, and DP-SeqGAN can greatly reduce the success rate of member inference attacks against the generated data set.