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Differential Privacy (DP) image data synthesis, which leverages the DPtechnique to generate synthetic data to replace the sensitive data, allowingorganizations to share and utilize synthetic images without privacy concerns.Previous methods incorporate the advanced techniques of generative models andpre-training on a public dataset to produce exceptional DP image data, butsuffer from problems of unstable training and massive computational resourcedemands. This paper proposes a novel DP image synthesis method, termedPRIVIMAGE, which meticulously selects pre-training data, promoting theefficient creation of DP datasets with high fidelity and utility. PRIVIMAGEfirst establishes a semantic query function using a public dataset. Then, thisfunction assists in querying the semantic distribution of the sensitivedataset, facilitating the selection of data from the public dataset withanalogous semantics for pre-training. Finally, we pre-train an image generativemodel using the selected data and then fine-tune this model on the sensitivedataset using Differentially Private Stochastic Gradient Descent (DP-SGD).PRIVIMAGE allows us to train a lightly parameterized generative model, reducingthe noise in the gradient during DP-SGD training and enhancing trainingstability. Extensive experiments demonstrate that PRIVIMAGE uses only 1% of thepublic dataset for pre-training and 7.6% of the parameters in the generativemodel compared to the state-of-the-art method, whereas achieves superiorsynthetic performance and conserves more computational resources. On average,PRIVIMAGE achieves 30.1% lower FID and 12.6% higher Classification Accuracythan the state-of-the-art method. The replication package and datasets can beaccessed online.

PrivImage: Differentially Private Synthetic Image Generation using Diffusion Models with Semantic-Aware Pretraining