Abstract: Abstract: Regarding the serious privacy breach risk caused by generative artificial intelligence，we focus on data privacy protection in generative artificial intelligence. This includes four topics: data privacy desensitization in generative AI， data privacy anonymization processing of generative AI， data privacy enhancement of generative AI， and intelligent monitoring and warning of abnormal data behavior using generative AI. Data desensitization is completed based on generative adversarial networks， the generator is capable of generating highly realistic data samples，however， the discriminator is difficult to distinguish authenticity， thereby enhancing the security of private data. Sensitive information of private data is anonymized based on k-anonymity algorithm to further improve the security of private data； the differential privacy technology of generative artificial intelligence model  is utilized to enhance data privacy and achieve the purpose of protecting private information； Formulate monitoring and early warning procedure for abnormal behavior during access， transmission and processing of private data is formulated to prevent the occurrence of security risk events of private data. The experimental results show that the maximum similarity between the generated data and the original privacy data reaches 98%， the minimum retention of sensitive information of privacy data reaches 3.21%， the maximum noise ratio of privacy data reaches 33%， and the area under ROC curve is larger.

Key words: Key words: privacy data, generative artificial intelligence model, data desensitization, big model, privacy enhancement