# Differentially Private Data Generative Models

arXiv: https://arxiv.org/abs/1812.02274 · HTML: https://mlsanity.com/paper/1812.02274/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2018.

## Built on by (most cited first, 30 of 33)

- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [Node-Level Membership Inference Attacks Against Graph Neural Networks](https://mlsanity.com/paper/2102.05429/) (2021)
- [EncoderMI: Membership Inference against Pre-trained Encoders in Contrastive Learning](https://mlsanity.com/paper/2108.11023/) (2021)
- [Synthetic Data -- what, why and how?](https://mlsanity.com/paper/2205.03257/) (2022)
- [Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation](https://mlsanity.com/paper/2211.13358/) (2022)
- [SoK: Privacy-Preserving Data Synthesis](https://mlsanity.com/paper/2307.02106/) (2023)
- [ADePT: Auto-encoder based Differentially Private Text Transformation](https://mlsanity.com/paper/2102.01502/) (2021)
- [Wide Network Learning with Differential Privacy](https://mlsanity.com/paper/2103.01294/) (2021)
- [Private GANs, Revisited](https://mlsanity.com/paper/2302.02936/) (2023)
- [SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learning](https://mlsanity.com/paper/2109.12062/) (2021)
- [DPD-fVAE: Synthetic Data Generation Using Federated Variational Autoencoders With Differentially-Private Decoder](https://mlsanity.com/paper/2211.11591/) (2022)
- [DP2-VAE: Differentially Private Pre-trained Variational Autoencoders](https://mlsanity.com/paper/2208.03409/) (2022)
- [Membership Inference Attacks on Knowledge Graphs](https://mlsanity.com/paper/2104.08273/) (2021)
- [Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey](https://mlsanity.com/paper/2209.09239/) (2022)
- [COLEP: Certifiably Robust Learning-Reasoning Conformal Prediction via Probabilistic Circuits](https://mlsanity.com/paper/2403.11348/) (2024)
- [On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulation](https://mlsanity.com/paper/2211.15809/) (2022)
- [Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilities](https://mlsanity.com/paper/2405.20959/) (2024)
- [Deep Generative Models, Synthetic Tabular Data, and Differential Privacy: An Overview and Synthesis](https://mlsanity.com/paper/2307.15424/) (2023)
- [An Empirical Study on the Membership Inference Attack against Tabular Data Synthesis Models](https://mlsanity.com/paper/2208.08114/) (2022)
- [Generating tabular datasets under differential privacy](https://mlsanity.com/paper/2308.14784/) (2023)
- [Tabular Data Synthesis with Differential Privacy: A Survey](https://mlsanity.com/paper/2411.03351/) (2024)
- [Mitigating Statistical Bias within Differentially Private Synthetic Data](https://mlsanity.com/paper/2108.10934/) (2021)
- [A Unified View of Differentially Private Deep Generative Modeling](https://mlsanity.com/paper/2309.15696/) (2023)
- [TraVaG: Differentially Private Trace Variant Generation Using GANs](https://mlsanity.com/paper/2303.16704/) (2023)
- [A Survey on Differential Privacy with Machine Learning and Future Outlook](https://mlsanity.com/paper/2211.10708/) (2022)
- [Differentially Private Normalizing Flows for Density Estimation, Data Synthesis, and Variational Inference with Application to Electronic Health Records](https://mlsanity.com/paper/2302.05787/) (2023)
- [Personalized Privacy-Preserving Framework for Cross-Silo Federated Learning](https://mlsanity.com/paper/2302.12020/) (2023)
- [Preserving Privacy in GANs Against Membership Inference Attack](https://mlsanity.com/paper/2311.03172/) (2023)
- [Towards a Game-theoretic Understanding of Explanation-based Membership Inference Attacks](https://mlsanity.com/paper/2404.07139/) (2024)

## Abstract (arXiv)

Deep neural networks (DNNs) have recently been widely adopted in various applications, and such success is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and access to a large amount of data. However, the large-scale data collections required for deep learning often contain sensitive information, therefore raising many privacy concerns. Prior research has shown several successful attacks in inferring sensitive training data information, such as model inversion, membership inference, and generative adversarial networks (GAN) based leakage attacks against collaborative deep learning. In this paper, to enable learning efficiency as well as to generate data with privacy guarantees and high utility, we propose a differentially private autoencoder-based generative model (DP-AuGM) and a differentially private variational autoencoder-based generative model (DP-VaeGM). We evaluate the robustness of two proposed models. We show that DP-AuGM can effectively defend against the model inversion, membership inference, and GAN-based attacks. We also show that DP-VaeGM is robust against the membership inference attack. We conjecture that the key to defend against the model inversion and GAN-based attacks is not due to differential privacy but the perturbation of training data. Finally, we demonstrate that both DP-AuGM and DP-VaeGM can be easily integrated with real-world machine learning applications, such as machine learning as a service and federated learning, which are otherwise threatened by the membership inference attack and the GAN-based attack, respectively.