  Differential Privacy (DP) image data synthesis, which leverages the DP
technique to generate synthetic data to replace the sensitive data, allowing
organizations to share and utilize synthetic images without privacy concerns.
Previous methods incorporate the advanced techniques of generative models and
pre-training on a public dataset to produce exceptional DP image data, but
suffer from problems of unstable training and massive computational resource
demands. This paper proposes a novel DP image synthesis method, termed
PRIVIMAGE, which meticulously selects pre-training data, promoting the
efficient creation of DP datasets with high fidelity and utility. PRIVIMAGE
first establishes a semantic query function using a public dataset. Then, this
function assists in querying the semantic distribution of the sensitive
dataset, facilitating the selection of data from the public dataset with
analogous semantics for pre-training. Finally, we pre-train an image generative
model using the selected data and then fine-tune this model on the sensitive
dataset using Differentially Private Stochastic Gradient Descent (DP-SGD).
PRIVIMAGE allows us to train a lightly parameterized generative model, reducing
the noise in the gradient during DP-SGD training and enhancing training
stability. Extensive experiments demonstrate that PRIVIMAGE uses only 1% of the
public dataset for pre-training and 7.6% of the parameters in the generative
model compared to the state-of-the-art method, whereas achieves superior
synthetic performance and conserves more computational resources. On average,
PRIVIMAGE achieves 30.1% lower FID and 12.6% higher Classification Accuracy
than the state-of-the-art method. The replication package and datasets can be
accessed online.
