ATLAS:基于gan的差分私有多方数据共享

IF 7.5 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zhenya Wang;Xiang Cheng;Sen Su;Jintao Liang;Haocheng Yang
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引用次数: 1

摘要

在这篇文章中,我们研究了差异隐私的多方数据共享问题,其中参与方在半诚实的策展人的协助下共同生成共享数据集,同时满足差异隐私。受生成对抗性网络(GAN)数据合成成功的启发,我们提出了一种新的基于GAN的差分私有多方数据共享方法ATLAS。在ATLAS中,我们将原始GAN扩展到多个鉴别器,并让每一方持有一个鉴别者,而策展人持有一个生成器。为了在不损害各方隐私的情况下更新生成器,我们分解生成器梯度的计算,并选择性地净化鉴别器的响应。此外,我们提出了两种提高共享数据效用的方法,即协作鉴别器滤波(CDF)方法和自适应梯度扰动(AGP)方法。具体而言,CDF方法利用经过训练的鉴别器来细化合成记录,而AGP方法在训练期间自适应地调整噪声规模,以减少不同私有噪声对最终共享数据的影响。在真实世界数据集上进行的大量实验验证了我们的ATLAS方法的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ATLAS: GAN-Based Differentially Private Multi-Party Data Sharing
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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来源期刊
CiteScore
11.80
自引率
2.80%
发文量
114
期刊介绍: The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.
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