论文标题
在安全联盟的安全联盟一般的对抗网络中生成合成数据
Generating Synthetic Data in a Secure Federated General Adversarial Networks for a Consortium of Health Registries
论文作者
论文摘要
在这项工作中，我们回顾了现有的联合一般对抗网络（GAN）解决方案的架构设计，并突出了现有设计中与安全性和信任相关的弱点。然后，我们描述了这些弱点如何使现有设计不适合用于为研究目的生成合成数据集的健康注册财团所需的要求。此外，我们建议如何通过我们的新型建筑解决方案来解决这些弱点。我们的新型体系结构解决方案结合了几个构件，以在分散的设置中生成合成数据。财团区块链，安全的多方计算和同型加密是我们建议的体系结构解决方案的核心构建基础，旨在解决联合gans现有设计中的弱点。最后，我们讨论了我们提出的解决方案的优势和未来的研究方向。
In this work, we review the architecture design of existing federated General Adversarial Networks (GAN) solutions and highlight the security and trust-related weaknesses in the existing designs. We then describe how these weaknesses make existing designs unsuitable for the requirements needed for a consortium of health registries working towards generating synthetic datasets for research purposes. Moreover, we propose how these weaknesses can be addressed with our novel architecture solution. Our novel architecture solution combines several building blocks to generate synthetic data in a decentralised setting. Consortium blockchains, secure multi-party computations, and homomorphic encryption are the core building blocks of our proposed architecture solution to address the weaknesses in the existing design of federated GANs. Finally, we discuss our proposed solution's advantages and future research directions.
