Differentially Private Releasing via Deep Generative Model (Technical Report)

Abstract

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.

Cited by in corpus (7)

• When Machine Learning Meets Privacy: A Survey and Outlook
• AdaCliP: Adaptive Clipping for Private SGD
• GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators
• Differentially Private Synthetic Data: Applied Evaluations and Enhancements
• DTGAN: Differential Private Training for Tabular GANs
• Effective and Privacy preserving Tabular Data Synthesizing
• DP-SGD vs PATE: Which Has Less Disparate Impact on GANs?