Abstract: The widespread application of data sharing and publication in the socio-economic domain drives scientific progress and societal development.However,issues related to copyright and privacy,especially concerning personal data,remain critical challenges.Differential privacy data synthesis has emerged as an effective means of protecting data privacy,where data holders can release synthetic data instead of real data,thereby enhancing data utility and availability while preserving privacy.In response to the limited usability of existing differential privacy generation models,this paper proposes a two-stage differential privacy generation model based on the latent space diffusion approach.Firstly,the differential privacy-aware information compression is performed on the original image,and it is projected from the pixel space to the latent space to obtain the desensitized latent vector representation of the original sensitive data.The latent vector is then fed into a diffusion model to gradually transform into a prior distribution and sampled through a denoising process.Experimental results based on the MNIST and Fashion MNIST datasets demonstrate that the proposed model exhibits significant improvements in terms of Fréchet inception distance(FID) and downstream task accuracy compared to state-of-the-art models like DP-Sinkhorn.

Key words: Differential privacy, Data synthesis, Generative models, Autoencoder, Diffusion models