Data to 7 Oct 2026

DP-CGAN: Differentially Private Synthetic Data and Label Generation

arXiv 2001.09700 ↗First posted 27 Jan 2020cs.LG stat.MLcited by 118

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Abstract (arXiv)

Generative Adversarial Networks (GANs) are one of the well-known models to generate synthetic data including images, especially for research communities that cannot use original sensitive datasets because they are not publicly accessible. One of the main challenges in this area is to preserve the privacy of individuals who participate in the training of the GAN models. To address this challenge, we introduce a Differentially Private Conditional GAN (DP-CGAN) training framework based on a new clipping and perturbation strategy, which improves the performance of the model while preserving privacy of the training dataset. DP-CGAN generates both synthetic data and corresponding labels and leverages the recently introduced Renyi differential privacy accountant to track the spent privacy budget. The experimental results show that DP-CGAN can generate visually and empirically promising results on the MNIST dataset with a single-digit epsilon parameter in differential privacy.

Key facts. Cited by 118 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Jan 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 27; 2023: 29; 2024: 22; 2025: 17; 2026: 6.

118 papers.

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2021Large Language Models Can Be Strong Differentially Private Learnerscited by 405
2022Differentially Private Diffusion Modelscited by 118
2023Machine Learning for Synthetic Data Generation: A Reviewcited by 118
2023Differentially Private Diffusion Models Generate Useful Synthetic Imagescited by 85
2022CTAB-GAN+: Enhancing Tabular Data Synthesiscited by 70
2022Differentially Private Language Models for Secure Data Sharingcited by 68
2023Membership Inference Attacks against Synthetic Data through Overfitting Detectioncited by 66
2021Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergencecited by 54
2021Differentially Private Query Release Through Adaptive Projectioncited by 50
2023SoK: Privacy-Preserving Data Synthesiscited by 37
2022Private Set Generation with Discriminative Informationcited by 34
2024Privacy-Preserving Instructions for Aligning Large Language Modelscited by 34

38 more

2021DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregationcited by 31
2023Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Datacited by 29
2024PrE-Text: Training Language Models on Private Federated Data in the Age of LLMscited by 29
2022Pre-trained Perceptual Features Improve Differentially Private Image Generationcited by 28
2023DP-LDMs: Differentially Private Latent Diffusion Modelscited by 28
2021PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learningcited by 23
2024How Private are DP-SGD Implementations?cited by 22
2021Hermite Polynomial Features for Private Data Generationcited by 21
2023From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modelingcited by 21
2021Perceptual Indistinguishability-Net (PI-Net): Facial Image Obfuscation with Manipulable Semanticscited by 20
2021Differentially Private Sliced Wasserstein Distancecited by 19
2023Generating Private Synthetic Data with Genetic Algorithmscited by 18
2024Differentially Private Fine-Tuning of Diffusion Modelscited by 18
2025Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraintscited by 18
2023Private GANs, Revisitedcited by 17
2024Differentially Private Tabular Data Synthesis using Large Language Modelscited by 16
2021SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learningcited by 15
2023Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generationcited by 15
2025A False Sense of Privacy: Evaluating Textual Data Sanitization Beyond Surface-level Privacy Leakagecited by 15
2022PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networkscited by 14
2022DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systemscited by 14
2022DPD-fVAE: Synthetic Data Generation Using Federated Variational Autoencoders With Differentially-Private Decodercited by 14
2023Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyondcited by 14
2021Differentially Private Imaging via Latent Space Manipulationcited by 13
2022DP2-VAE: Differentially Private Pre-trained Variational Autoencoderscited by 13
2023DPMLBench: Holistic Evaluation of Differentially Private Machine Learningcited by 13
2023Unified Enhancement of Privacy Bounds for Mixture Mechanisms via f-Differential Privacycited by 13
2022Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Datacited by 12
2023CuTS: Customizable Tabular Synthetic Data Generationcited by 12
2023PrivImage: Differentially Private Synthetic Image Generation using Diffusion Models with Semantic-Aware Pretrainingcited by 12
2024Privacy Vulnerabilities in Marginals-based Synthetic Datacited by 10
2022GeoPointGAN: Synthetic Spatial Data with Local Label Differential Privacycited by 9
2022On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulationcited by 8
2024Privacy-preserving data release leveraging optimal transport and particle gradient descentcited by 8
2021DTGAN: Differential Private Training for Tabular GANscited by 7
2022Differentially Private Generative Adversarial Networks with Model Inversioncited by 7
2025Struct-Bench: A Benchmark for Differentially Private Structured Text Generationcited by 7
2021DP-SGD vs PATE: Which Has Less Disparate Impact on GANs?cited by 6

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