鉴别器
计算机科学
发电机(电路理论)
差别隐私
地图集(解剖学)
噪音(视频)
数据共享
对抗制
合成数据
生成对抗网络
数据挖掘
人工智能
深度学习
电信
物理
医学
古生物学
功率(物理)
替代医学
病理
量子力学
探测器
生物
图像(数学)
作者
Zhenya Wang,Xiang Cheng,Sen Su,Jintao Liang,Hao-Cheng Yang
出处
期刊:IEEE Transactions on Big Data
[Institute of Electrical and Electronics Engineers]
日期:2023-05-18
卷期号:9 (4): 1225-1237
被引量:5
标识
DOI:10.1109/tbdata.2023.3277716
摘要
In this article, we study the problem of differentially private multi-party data sharing, where the involved parties assisted by a semi-honest curator collectively generate a shared dataset while satisfying differential privacy. Inspired by the success of data synthesis with the generative adversarial network (GAN), we propose a novel GAN-based differentially private multi-party data sharing approach named ATLAS. In ATLAS, we extend the original GAN to multiple discriminators, and let each party hold a discriminator while the curator holds a generator. To update the generator without compromising each party's privacy, we decompose the calculation of the generator's gradient and selectively sanitize the discriminators' responses . Additionally, we propose two methods to improve the utility of shared data, i.e., the collaborative discriminator filtering (CDF) method and the adaptive gradient perturbation (AGP) method. Specifically, the CDF method utilizes trained discriminators to refine synthetic records, while the AGP method adaptively adjusts the noise scale during training to reduce the impact of deferentially private noise on the final shared data. Extensive experiments on real-world datasets validate the superiority of our ATLAS approach.
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