GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators (2006.08265v2)

Abstract: The wide-spread availability of rich data has fueled the growth of machine learning applications in numerous domains. However, growth in domains with highly-sensitive data (e.g., medical) is largely hindered as the private nature of data prohibits it from being shared. To this end, we propose Gradient-sanitized Wasserstein Generative Adversarial Networks (GS-WGAN), which allows releasing a sanitized form of the sensitive data with rigorous privacy guarantees. In contrast to prior work, our approach is able to distort gradient information more precisely, and thereby enabling training deeper models which generate more informative samples. Moreover, our formulation naturally allows for training GANs in both centralized and federated (i.e., decentralized) data scenarios. Through extensive experiments, we find our approach consistently outperforms state-of-the-art approaches across multiple metrics (e.g., sample quality) and datasets.

GS-WGAN: Enhancing Differentially Private Generation through Gradient Sanitization

The paper introduces a novel approach termed Gradient-Sanitized Wasserstein Generative Adversarial Networks (GS-WGAN) that seeks to address the challenge of generating differentially private synthetic data from sensitive datasets. This approach is particularly useful in domains where data privacy is paramount yet the release of detailed data is necessary for advancing machine learning techniques.

Overview and Methodology

The GS-WGAN builds upon the framework of Generative Adversarial Networks (GANs), specifically focusing on improving differential privacy guarantees while maintaining data utility. The core takeaway from the paper is the introduction of a gradient sanitization technique which more effectively distorts gradient information during training, compared to existing strategies. This is achieved by selectively applying privacy-preserving mechanisms only to the generator components as opposed to the entire network, leveraging the discriminator's functionality purely during the training phase.

The GS-WGAN utilizes the Wasserstein distance, incorporating a gradient penalty term, to allow precise estimation of gradient norms and sensitivity values—a critical aspect that aids in retaining sample quality even when privacy-preserving alterations are introduced. This method dispenses with cumbersome hyper-parameter tuning, thereby simplifying the differential privacy adherence without considerable loss in sample fidelity.

Experimental Insights

The experimental component of the paper convincingly demonstrates that GS-WGAN outperforms current state-of-the-art privacy-preserving generative models across various datasets and benchmarks. The improvements are measured using metrics such as Inception Score (IS) and Fréchet Inception Distance (FID), which affirm enhanced sample quality in high-dimensional datasets like MNIST and Fashion-MNIST. Furthermore, the utility of generated data for downstream tasks is evaluated, showing notable improvements in classification tasks, presenting a significant increase in accuracy across a range of models such as Multi-layer Perceptrons (MLP) and Convolutional Neural Networks (CNN).

Implications and Future Developments

The practical implications of GS-WGAN are far-reaching. By offering a rigorous differential privacy framework, this approach could facilitate the secure sharing of medical, financial, and other sensitive datasets. The ability to generate synthetic data that retains utility while guaranteeing privacy can significantly accelerate the advancement of ML applications within constrained domains.

Theoretically, the introduction of gradient sanitization within the GAN structure paves the way for exploring larger and more complex network architectures under privacy constraints, potentially evolving the design of privacy-preserving mechanisms in federated learning setups.

Speculations and Future Work

Future research directions could explore the integration of GS-WGAN with other types of generative networks beyond GANs, such as Variational Autoencoders (VAEs), to measure the cross-compatibility and effectivity of gradient sanitization techniques in different model architectures. Additionally, the scalability of GS-WGAN towards real-time applications or those requiring continuous data generation and privacy preservation could warrant deeper investigation.

In conclusion, the GS-WGAN represents a significant milestone in the pursuit of balancing data utility and privacy, offering a scalable, less intrusive means to sanitize data for secure sharing and analytical purposes. The insights offered by the paper could inspire further explorations into privacy-preserving machine learning, fostering an environment that supports both innovation and individual data protection.