With the rapid development of the mobile Internet industry， user data and browsing data have increased significantly， so it is extremely important to accurately grasp the potential needs of users and improve the effect of advertisement recommendation. As a relatively advanced recommendation method at present， DeepFM model can extract various complexity features from the original features， but the model does not protect the data. In order to realize the privacy protection in DeepFM model， a new DeepFM model based on Differential Privacy （DP） was proposed， namely DP-DeepFM. The Gaussian noise was added to Adam optimization algorithm in the training process of DP-DeepFM and the gradient clipping was performed to prevent the addition of excessive noise causing poor model performance. Experimental results on advertising dataset Criteo show that compared with DeepFM， DP-DeepFM only has the accuracy decreased by 0.44 percentage points， but it provides differential privacy protection and is more secure.