Source-linked AI summary
Differentially Private Generative Adversarial Network
Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, Jiayu Zhou
TL;DR
Limited and sensitive medical data motivate generative models, but GANs may concentrate their distributions around training samples and disclose private information. The paper proposes DPGAN, which applies noise during gradient-based training to provide differential privacy. Across benchmark settings, it reports high-quality generated data with sufficient privacy protection at a reasonable privacy budget.
Problem
Medical data are limited and sensitive, while GANs can concentrate generated distributions around training samples and implicitly disclose private information.
Method
DPGAN applies carefully designed noise and gradient clipping to Wasserstein-distance gradients during training, with moments accounting used to bound privacy loss.
Results
DPGAN generates high-quality data with sufficient differential privacy protection across benchmark datasets and network structures at a reasonable privacy budget.
Takeaways & Limitations
The framework supports generating arbitrarily many data points while preserving the differential privacy of the training data.
Takeaways & Limitations
The privacy analysis assumes the neighboring mechanisms have generally identical supports, and the method relies on bounded data points and weights.
Abstract
from arXiv · showhide
Generative Adversarial Network (GAN) and its variants have recently attracted intensive research interests due to their elegant theoretical foundation and excellent empirical performance as generative models. These tools provide a promising direction in the studies where data availability is limited. One common issue in GANs is that the density of the learned generative distribution could concentrate on the training data points, meaning that they can easily remember training samples due to the high model complexity of deep networks. This becomes a major concern when GANs are applied to private or sensitive data such as patient medical records, and the concentration of distribution may divulge critical patient information. To address this issue, in this paper we propose a differentially private GAN (DPGAN) model, in which we achieve differential privacy in GANs by adding carefully designed noise to gradients during the learning procedure. We provide rigorous proof for the privacy guarantee, as well as comprehensive empirical evidence to support our analysis, where we demonstrate that our method can generate high quality data points at a reasonable privacy level.
1 INTRODUCTION
The paper motivates DPGAN by combining GAN-based data generation with differential privacy to address data scarcity and training-sample disclosure. It proposes noisy-gradient training with privacy guarantees and reports high-quality generation at a reasonable privacy level.
- Medical analytics remains challenging because patient data are scarce, inaccessible for privacy reasons, and costly to collect.
- GANs can generate realistic samples from limited training data, but their learned distributions may concentrate around training points.
- Repeated sampling from GANs can recover training samples, creating a need for models that jointly preserve sample quality and privacy.
- DPGAN adds carefully designed noise and gradient clipping during training, using Wasserstein distance within its privacy-preserving framework.
- DPGAN is evaluated across benchmark datasets and fully connected and convolutional networks, generating high-quality data with differential privacy at a reasonable privacy budget.
2 RELATED WORK
The related work situates DPGAN among advances in GAN stability, Wasserstein-based objectives, and differentially private learning. It distinguishes the proposed framework by applying privacy protection during training while avoiding several prior privacy costs and leakage risks.
- Generative Adversarial Network: Prior GAN research includes gradient penalties, energy-based discriminators, equilibrium enforcement, and loss-sensitive objectives to improve stability, diversity, or visual quality.
- Differentially Private Learning: Differentially private learning has explored objective perturbation and noise added to stochastic-gradient updates, with larger batches improving empirical performance in one study.
- DPGAN: DPGAN addresses prior challenges by training a differentially private generator, avoiding privacy loss proportional to public labeled data and enabling unlimited generated samples.
- DPGAN: DPGAN also avoids transmitting updated local parameters and adds noise within training rather than perturbing energy functions and an extra softmax layer.
3 METHODOLOGY
DPGAN preserves privacy during GAN training by perturbing clipped gradients and uses moments accounting to bound privacy loss. The framework builds on WGAN and establishes differential privacy for the learned generator through the discriminator’s private parameters.
- Privacy accounting: Moments accountant bounds privacy across iterative gradient updates and relates the required noise scale to the target privacy level.Its composability property bounds the overall accountant using per-iteration contributions.
- DPGAN framework: DPGAN applies carefully designed gradient noise and clipping within a WGAN-based framework to protect training data.The approach uses Wasserstein distance and exploits WGAN’s Lipschitz structure.
- DPGAN framework: The method protects privacy during training rather than perturbing final parameters, which the authors associate with low utility.Noise is added to gradients of the Wasserstein distance with respect to training data.
- Privacy accounting: The discriminator update is treated as a differentially private algorithm, and repeated updates are analyzed as an adaptive composition.The previous discriminator parameters act as auxiliary input, while the updated parameters are the algorithm output.
- Privacy accounting: Larger sampling rate q and more discriminator iterations reduce privacy for fixed gradient perturbation, requiring careful parameter selection.More data per update assigns less privacy to each point, while more iterations expose more gradient information.
- Privacy guarantees: The learned generator satisfies (ϵ,δ)-differential privacy because generator parameters are obtained through post-processing of private discriminator parameters.The theorem states that the generator output from Algorithm 1 guarantees (ϵ,δ)-differential privacy.
4 EXPERIMENT
Experiments on MNIST and MIMIC-III show that DPGAN preserves privacy while maintaining useful generation quality, with stronger privacy generally reducing fidelity and increasing training fluctuations.
- Experimental setup: DPGAN experiments on MNIST and MIMIC-III evaluate how privacy noise affects generation quality, convergence, and downstream utility.The study uses Wasserstein distance, image inspection, classification, distribution-wise prediction, and EHR evaluations.
- Relationship between Privacy Level and Generation Performance: On MNIST, adding more noise makes generated images blurrier, while less noise generally yields higher classifier accuracy and better generated-data quality.For digit pair 01, quality is reported as little affected below a threshold somewhere between 3.0 and 11.0, motivating an ϵ above that threshold.
- Relationship between Privacy Level and the Convergence of Network: Wasserstein distance decreases and converges during training, but smaller ϵ produces more frequent fluctuations and larger late-stage variance.These fluctuations are attributed partly to noisy min-max training, while peaks can be eliminated during training without divergence.
- Overall findings: Across the experiments, DPGAN demonstrates a trade-off between privacy and learning performance while retaining useful data quality at reasonable privacy levels.The EHR results also report that noise perturbs disease rarity, helping protect sensitive distributional information.
- Electronic Health Records: On MIMIC-III, larger noise makes generated disease distributions deviate further from real data, indicating degraded distribution approximation.Figure 4 compares real and generated Bernoulli success probabilities for each disease dimension; points concentrate near y = x with less noise.
- Classification on EHR Data: On MIMIC-III, generated-data classifier AUC decreases and data become sparser as ϵ decreases, reflecting weaker capture of inter-dimensional relationships under stronger privacy.Noise perturbs discriminator training and indirectly shifts the generator’s output distribution; nevertheless, the paper reports limited overall performance degradation.
5 CONCLUSION
The paper proposes DPGAN, a privacy-preserving GAN with rigorous differential privacy guarantees. Experiments show good-quality data generation and convergence despite noise and limited training data.
- DPGAN preserves training-data privacy in the differential privacy sense and rigorously guarantees (ϵ,δ)-differential privacy.
- Experiments show that DPGAN generates good-quality data points under noisy training and limited training data.
- The experiments also demonstrate convergence and meaningful learning curves for tuning hyperparameters.
- Future work will seek to reduce the privacy budget through alternative clipping methods and tighten the utility bound.