The machine learning algorithm is gradually being applied to various fields and has become the core technology to achieve artificial intelligence. The success of machine learning cannot be achieved without the support of large amounts of data and computing power, which are usually collected through crowdsourcing and learned online. The data collected for machine learning training often contains some personal and sensitive information, including personal mobile phone numbers, ID numbers, and medical information. How to protect these private data at low cost and efficiently is an important problem. Aiming at this kind of problem, this article starts with the privacy problem in machine learning and the way of being attacked and summarizes the privacy protection methods and characteristics in the machine learning algorithm. Then, for the classification accuracy of the different algorithm that uses noise to protect privacy, a deep difference privacy protection method combined with a convolutional neural network is proposed. This method perfectly integrates the features of difference and Gaussian distribution and can obtain the privacy budget of each layer of the neural network. Finally, the stochastic gradient descent algorithm's gradient value is employed to set the Gaussian noise scale and preserve the data's sensitive information. The experimental results demonstrated that by adjusting the parameters of the depth differential privacy model based on differences in private information in the data, a balance between the availability and privacy protection of the training data set could be reached.