We propose a new framework of synthesizing data using deep generative models
in a differentially private manner. Within our framework, sensitive data are
sanitized with rigorous privacy guarantees in a one-shot fashion, such that
training deep generative models is possible without re-using the original data.
Hence, no extra privacy costs or model constraints are incurred, in contrast to
popular approaches such as Differentially Private Stochastic Gradient Descent
(DP-SGD), which, among other issues, causes degradation in privacy guarantees
as the training iteration increases. We demonstrate a realization of our
framework by making use of the characteristic function and an adversarial
re-weighting objective, which are of independent interest as well. Our proposal
has theoretical guarantees of performance, and empirical evaluations on
multiple datasets show that our approach outperforms other methods at
reasonable levels of privacy.