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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 theanalysis task, excessive sanitization is often necessary to ensure privacy,leading to significant loss of the data utility. In this paper, we presentdp-GAN, a general private releasing framework for semantic-rich data. Insteadof sanitizing and then releasing the data, the data curator publishes a deepgenerative model which is trained using the original data in a differentiallyprivate manner; with the generative model, the analyst is able to produce anunlimited amount of synthetic data for arbitrary analysis tasks. In contrast ofalternative solutions, dp-GAN highlights a set of key features: (i) it providestheoretical privacy guarantee via enforcing the differential privacy principle;(ii) it retains desirable utility in the released model, enabling a variety ofotherwise impossible analyses; and (iii) most importantly, it achievespractical training scalability and stability by employing multi-foldoptimization strategies. Through extensive empirical evaluation on benchmarkdatasets and analyses, we validate the efficacy of dp-GAN.

Differentially Private Releasing via Deep Generative Model (Technical Report)