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

Pei Hsuan Lu, Chia Mu Yu

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題CCS 2017 - Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security
發行者Association for Computing Machinery
頁面2547-2549
頁數3
ISBN(電子)9781450349468
DOIs
出版狀態Published - 30 十月 2017
事件24th ACM SIGSAC Conference on Computer and Communications Security, CCS 2017 - Dallas, United States
持續時間: 30 十月 20173 十一月 2017

出版系列

名字Proceedings of the ACM Conference on Computer and Communications Security
ISSN(列印)1543-7221

Conference

Conference24th ACM SIGSAC Conference on Computer and Communications Security, CCS 2017
國家United States
城市Dallas
期間30/10/173/11/17

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