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https://github.com/astorfi/differentially-private-cgan

https://github.com/astorfi/differentially-private-cgan

Science Score: 23.0%

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Repository

Basic Info
  • Host: GitHub
  • Owner: astorfi
  • License: mit
  • Language: Python
  • Default Branch: master
  • Size: 1.05 MB
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  • Stars: 13
  • Watchers: 3
  • Forks: 7
  • Open Issues: 1
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Created about 6 years ago · Last pushed over 5 years ago
Metadata Files
Readme License

README.md

Differentially Private Synthetic Medical Data Generation using Convolutional GANs

arXiv

This repository contains an implementation of "Differentially Private Synthetic Medical Data Generation using Convolutional GANs".

For a detailed description of the architecture please read our paper. Using the code of this repository is allowed with proper attribution: Please cite the paper if you use the code from this repository in your work.

Bibtex

@misc{torfi2020differentially,
  title={Differentially Private Synthetic Medical Data Generation using Convolutional GANs}, 
  author={Amirsina Torfi and Edward A. Fox and Chandan K. Reddy},
  year={2020},
  eprint={2012.11774},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}

Table of contents

Paper Summary

Abstract *Deep learning models have demonstrated superior performance in several application problems, such as image classification and speech processing. However, creating a deep learning model using health record data requires addressing certain privacy challenges that bring unique concerns to researchers working in this domain. One effective way to handle such private data issues is to generate realistic synthetic data that can provide practically acceptable data quality and correspondingly the model performance. To tackle this challenge, we develop a differentially private framework for synthetic data generation using Rényi differential privacy. Our approach builds on convolutional autoencoders and convolutional generative adversarial networks to preserve some of the critical characteristics of the generated synthetic data. In addition, our model can also capture the temporal information and feature correlations that might be present in the original data. We demonstrate that our model outperforms existing state-of-the-art models under the same privacy budget using several publicly available benchmark medical datasets in both supervised and unsupervised settings.*

Running the Code

Prerequisites

  • Pytorch
  • CUDA [strongly recommended]

NOTE: PyTorch does a pretty good job in installing required packages but you should have installed CUDA according to PyTorch requirements. Please refer to this link for further information.

Collaborators

|
Amirsina Torfi | --- |

Owner

  • Name: Sina Torfi
  • Login: astorfi
  • Kind: user
  • Location: San Jose
  • Company: Meta

PhD & Developer working on Deep Learning, Computer Vision & NLP

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