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Privacy amplification means that the communication parties extract a shorter but highly confidential string [S] by negotiating on the public channel while sharing a partially confidential string S. Enemy only knows part of the information of the string S and the information that it knows about [S] is almost negligible. Recently, people use the generative adversarial networks (GANs) to realize the secure communication with the present of the adversary. This paper proposes to use the generative adversarial network to achieve a privacy amplification scheme when the adversary ability is limited. First, this paper proposes a privacy amplification implementation scenario. The two parties use the conversation information to generate identical keys, and the adversary listens to the conversation information. Then, with reference to the neural network structure in the basic encrypted communication model of Abadi et al., a privacy amplified communication model is built. The experiment tests the privacy amplified communication when the enemy knows part of the information or the opponent's computing power is weak. By modifying the activation function, increasing the complexity of the model and modifying the filter of convolutional neural network, the final results show that when the adversary gets 70% of the communication information, or when the communicator is more complex than the adversary model, both parties can negotiate a secure key to complete the function of security enhancement.