  Artificial intelligence and machine learning have been integrated into all
aspects of our lives and the privacy of personal data has attracted more and
more attention. Since the generation of the model needs to extract the
effective information of the training data, the model has the risk of leaking
the privacy of the training data. Membership inference attacks can measure the
model leakage of source data to a certain degree. In this paper, we design a
privacy-preserving generative framework against membership inference attacks,
through the information extraction and data generation capabilities of the
generative model variational autoencoder (VAE) to generate synthetic data that
meets the needs of differential privacy. Instead of adding noise to the model
output or tampering with the training process of the target model, we directly
process the original data. We first map the source data to the latent space
through the VAE model to get the latent code, then perform noise process
satisfying metric privacy on the latent code, and finally use the VAE model to
reconstruct the synthetic data. Our experimental evaluation demonstrates that
the machine learning model trained with newly generated synthetic data can
effectively resist membership inference attacks and still maintain high
utility.
