DP$^2$-VAE: Differentially Private Pre-trained Variational Autoencoders. (arXiv:2208.03409v2 [cs.LG] UPDATED)

Modern machine learning systems achieve great success when trained on large
datasets. However, these datasets usually contain sensitive information (e.g.
medical records, face images), leading to serious privacy concerns.
Differentially private generative models (DPGMs) emerge as a solution to
circumvent such privacy concerns by generating privatized sensitive data.
Similar to other differentially private (DP) learners, the major challenge for
DPGM is also how to achieve a subtle balance between utility and privacy. We
propose DP$^2$-VAE, a novel training mechanism for …

arxiv variational autoencoders