Differentially private synthetic medical data generation using convolutional gans
| dc.contributor.author | Torfi, Amirsina | en |
| dc.contributor.author | Fox, Edward A. | en |
| dc.contributor.author | Reddy, Chandan K. | en |
| dc.date.accessioned | 2024-01-22T13:05:33Z | en |
| dc.date.available | 2024-01-22T13:05:33Z | en |
| dc.date.issued | 2022 | en |
| dc.description.abstract | Deep learning models have demonstrated superior performance in several real-world application problems such as image classification and speech processing. However, creating these models in sensitive domains like healthcare typically requires addressing certain privacy challenges that bring unique concerns. One effective way to handle such private data concerns is to generate realistic synthetic data that can provide practically acceptable data quality as well as be used to improve 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 critical characteristics of the generated synthetic data. In addition, our model can capture the temporal information and feature correlations 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. The source code of this work is available at https://github.com/astorfi/differentially-private-cgan. | en |
| dc.description.version | Submitted version | en |
| dc.format.extent | Pages 485-500 | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | https://doi.org/10.1016/j.ins.2021.12.018 | en |
| dc.identifier.eissn | 1872-6291 | en |
| dc.identifier.issn | 0020-0255 | en |
| dc.identifier.orcid | Reddy, Chandan [0000-0003-2839-3662] | en |
| dc.identifier.orcid | Fox, Edward [0000-0003-1447-6870] | en |
| dc.identifier.uri | https://hdl.handle.net/10919/117430 | en |
| dc.identifier.volume | 586 | en |
| dc.language.iso | en | en |
| dc.publisher | Elsevier | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Deep learning | en |
| dc.subject | differential privacy | en |
| dc.subject | synthetic data generation | en |
| dc.subject | generative adversarial networks | en |
| dc.title | Differentially private synthetic medical data generation using convolutional gans | en |
| dc.title.serial | Information Sciences | en |
| dc.type | Article | en |
| dc.type.dcmitype | Text | en |
| dc.type.other | article | en |
| pubs.organisational-group | /Virginia Tech | en |
| pubs.organisational-group | /Virginia Tech/Engineering | en |
| pubs.organisational-group | /Virginia Tech/Engineering/Computer Science | en |
| pubs.organisational-group | /Virginia Tech/Faculty of Health Sciences | en |
| pubs.organisational-group | /Virginia Tech/All T&R Faculty | en |
| pubs.organisational-group | /Virginia Tech/Engineering/COE T&R Faculty | en |