  Privacy-preserving releasing of complex data (e.g., image, text, audio)
represents a long-standing challenge for the data mining research community.
Due to rich semantics of the data and lack of a priori knowledge about the
analysis task, excessive sanitization is often necessary to ensure privacy,
leading to significant loss of the data utility. In this paper, we present
dp-GAN, a general private releasing framework for semantic-rich data. Instead
of sanitizing and then releasing the data, the data curator publishes a deep
generative model which is trained using the original data in a differentially
private manner; with the generative model, the analyst is able to produce an
unlimited amount of synthetic data for arbitrary analysis tasks. In contrast of
alternative solutions, dp-GAN highlights a set of key features: (i) it provides
theoretical privacy guarantee via enforcing the differential privacy principle;
(ii) it retains desirable utility in the released model, enabling a variety of
otherwise impossible analyses; and (iii) most importantly, it achieves
practical training scalability and stability by employing multi-fold
optimization strategies. Through extensive empirical evaluation on benchmark
datasets and analyses, we validate the efficacy of dp-GAN.
