Open AccessProceedings Article
PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees
James Jordon,Jinsung Yoon,Mihaela van der Schaar +2 more
- 27 Sep 2018
TL;DR: This paper investigates a method for ensuring (differential) privacy of the generator of the Generative Adversarial Nets (GAN) framework, and modifies the Private Aggregation of Teacher Ensembles (PATE) framework and applies it to GANs.
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Abstract: Machine learning has the potential to assist many communities in using the large datasets that are becoming more and more available. Unfortunately, much of that potential is not being realized because it would require sharing data in a way that compromises privacy. In this paper, we investigate a method for ensuring (differential) privacy of the generator of the Generative Adversarial Nets (GAN) framework. The resulting model can be used for generating synthetic data on which algorithms can be trained and validated, and on which competitions can be conducted, without compromising the privacy of the original dataset. Our method modifies the Private Aggregation of Teacher Ensembles (PATE) framework and applies it to GANs. Our modified framework (which we call PATE-GAN) allows us to tightly bound the influence of any individual sample on the model, resulting in tight differential privacy guarantees and thus an improved performance over models with the same guarantees. We also look at measuring the quality of synthetic data from a new angle; we assert that for the synthetic data to be useful for machine learning researchers, the relative performance of two algorithms (trained and tested) on the synthetic dataset should be the same as their relative performance (when trained and tested) on the original dataset. Our experiments, on various datasets, demonstrate that PATE-GAN consistently outperforms the stateof-the-art method with respect to this and other notions of synthetic data quality.
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Citations
GAN-Based Data Augmentation For Improving The Classification Of EEG Signals
Sudhanva Bhat,Enrique Hortal +1 more
- 29 Jun 2021
TL;DR: In this article, a Wasserstein Generative Adversarial Network with gradient penalty (WGAN-GP) based model was proposed to enhance the accuracy scores by generating synthetic features that are close to actual data distribution.
Generating Synthetic Population Using Transformer Based Networks
Phattranit Phattharajiranan,V. Muangsin +1 more
- 14 Sep 2023
TL;DR: A novel Transformer-based approach for generating both numerical and categorical demographic attributes in synthetic populations that achieves good fit with minor errors on the test set and preserves population heterogeneity, as indicated by the number of unique individuals in the real and synthetic populations.
Privacy-Preserving Data Sharing in Agriculture: Enforcing Policy Rules for Secure and Confidential Data Synthesis
Anantaa Kotal,Lavanya Elluri,Deepti Gupta,Varun Mandalapu,Anupam Joshi +4 more
- 01 Jan 2023
TL;DR: Privacy-preserving data sharing in agriculture requires the enforcement of privacy policy rules to ensure the security and confidentiality of data. This study proposes a novel framework for enforcing privacy policy rules in privacy-preserving data generation algorithms.
DPGOMI: Differentially Private Data Publishing with Gaussian Optimized Model Inversion
TL;DR: This paper proposes a novel differentially private data releasing method called DPGOMI, which outperforms the standard DP-GAN method in terms of Inception Score, Fréchet Inception Distance, and classification performance, while providing the same level of privacy.
Machine Learning for Synthetic Data Generation: a Review
TL;DR: In this paper , a comprehensive systematic review of existing studies that employ machine learning models for the purpose of generating synthetic data is presented, starting with the applications of synthetic data generation, spanning computer vision, speech, natural language processing, healthcare, and business domains.
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