Poster: A unified framework of differentially private synthetic data release with generative adversarial network

Pei Hsuan Lu, Chia Mu Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Many differentially private data release solutions have been proposed for different types of data with the sacrifice of inherent correlation structure. Here, we propose a unified framework of releasing differentially private data. In particular, our proposed generative adversarial network (GAN)-based framework learns the input distribution, irrespective of tabular data and graphs, and generates synthetic data in a differentially private manner. Our preliminary results show the acceptable utility of the synthetic dataset.

Original languageEnglish
Title of host publicationCCS 2017 - Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security
PublisherAssociation for Computing Machinery
Pages2547-2549
Number of pages3
ISBN (Electronic)9781450349468
DOIs
StatePublished - 30 Oct 2017
Event24th ACM SIGSAC Conference on Computer and Communications Security, CCS 2017 - Dallas, United States
Duration: 30 Oct 20173 Nov 2017

Publication series

NameProceedings of the ACM Conference on Computer and Communications Security
ISSN (Print)1543-7221

Conference

Conference24th ACM SIGSAC Conference on Computer and Communications Security, CCS 2017
CountryUnited States
CityDallas
Period30/10/173/11/17

Cite this

Lu, P. H., & Yu, C. M. (2017). Poster: A unified framework of differentially private synthetic data release with generative adversarial network. In CCS 2017 - Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (pp. 2547-2549). (Proceedings of the ACM Conference on Computer and Communications Security). Association for Computing Machinery. https://doi.org/10.1145/3133956.3138823