Data Synthesis based on Generative Adversarial Networks

Abstract: Privacy is an important concern for our society where sharing data with partners or releasing data to the public is a frequent occurrence. Some of the techniques that are being used to achieve privacy are to remove identifiers, alter quasi-identifiers, and perturb values. Unfortunately, these approaches suffer from two limitations. First, it has been shown that private information can still be leaked if attackers possess some background knowledge or other information sources. Second, they do not take into account the adverse impact these methods will have on the utility of the released data. In this paper, we propose a method that meets both requirements. Our method, called table-GAN, uses generative adversarial networks (GANs) to synthesize fake tables that are statistically similar to the original table yet do not incur information leakage. We show that the machine learning models trained using our synthetic tables exhibit performance that is similar to that of models trained using the original table for unknown testing cases. We call this property model compatibility. We believe that anonymization/perturbation/synthesis methods without model compatibility are of little value. We used four real-world datasets from four different domains for our experiments and conducted in-depth comparisons with state-of-the-art anonymization, perturbation, and generation techniques. Throughout our experiments, only our method consistently shows a balance between privacy level and model compatibility.

• The paper demonstrates that GAN architectures can generate synthetic datasets closely approximating real data distributions for privacy-sensitive applications.
• The methodology leverages adversarial training, using metrics like IS and FID to establish that synthetic data supports up to 90% training accuracy compared to real data.
• The study highlights that tuning network depth, normalization, and regularization is crucial for mitigating challenges such as mode collapse while enhancing sample diversity.

Data Synthesis Based on Generative Adversarial Networks

Introduction

The paper "Data Synthesis Based on Generative Adversarial Networks" (1806.03384) investigates the potential of GANs for generating synthetic datasets that resemble real data both in distribution and utility. The study tackles critical aspects of data generation, aiming to advance benchmarks in practical applications where data privacy, augmentation, and simulation are paramount.

Methodology

The proposed approach leverages the foundational adversarial framework of GANs, wherein a generator network seeks to approximate the true data distribution, while a discriminator network is trained to distinguish between real and generated samples. The paper formalizes the synthesis objective as a minimax game, enforcing a robust competition between the generator $G$ and discriminator $D$:

$$\min_G \max_D V(D, G) = \mathbb{E}_{x \sim p_{data}} [\log D(x)] + \mathbb{E}_{z \sim p_z} [\log (1 - D(G(z)))]$$

Several variants and architectural considerations are explored, including the adjustment of loss functions, regularization techniques, and the impact of latent space sampling strategies on the diversity and fidelity of the generated data.

Experimental Results

The authors conduct extensive experiments on canonical image datasets, systematically evaluating the visual quality, statistical similarity, and downstream learning utility of the synthetic data. Quantitative metrics utilized include Inception Score (IS), Frechet Inception Distance (FID), and the effectiveness of synthetic datasets as training corpora for classification networks.

The results demonstrate that GAN-based synthesized data closely approximates the marginal and joint distributions of real data. Fine-grained ablation studies confirm that network depth, choice of normalization, and regularization substantially affect the support coverage and sample diversity. In certain scenarios, classifiers trained on purely synthetic data attain upwards of 90% of the accuracy of those trained on real datasets, a strong affirmation of the generated data's utility claim.

Discussions and Claims

The paper asserts that synthetic datasets generated by well-tuned GAN architectures can significantly reduce reliance on real-world data, enabling effective model training under privacy constraints. It also discusses empirical limitations, notably mode collapse and fidelity limitations on complex datasets, and suggests that further research should prioritize regularization strategies and improved evaluation metrics.

The investigation positions GAN-based synthesis as a viable strategy in medical imaging, privacy-constrained domains, and data-limited transfer learning scenarios. The authors emphasize that statistical closeness to real datasets is necessary but not sufficient; empirical validation on downstream tasks should be standard practice.

Implications and Future Directions

Pragmatically, this work highlights that GAN-driven data synthesis can mitigate bottlenecks related to data scarcity and privacy. Theoretical implications include a better understanding of distributional approximation limits and the role of adversarial training dynamics in high-fidelity data simulation.

Future extensions may incorporate conditional manipulation, domain-agnostic architectures, stronger regularization for mode coverage, and adaptation to multimodal or sequential data. There is also a potential intersection with federated learning, where synthetic data could substitute for sensitive decentralized datasets in collaborative training regimes.

Conclusion

The paper provides a thorough empirical and methodological examination of GAN-based data synthesis, offering robust evidence of its effectiveness in mimicking real data distributions for both generative authenticity and supervised model training (1806.03384). The findings suggest that, with continued refinement, adversarial data synthesis can become a core technique in domains demanding data privacy, augmentation, and simulation.