Demonstration of Dealer: An End-to-End Model Marketplace with Differential Privacy
Demonstration of Dealer: An End-to-End Model Marketplace with Differential Privacy
复制标题
Dealer演示：具有差异化隐私的端到端模型市场
DOI：
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发表时间：
2021
影响因子：
2.5
通讯作者：
Jimeng Sun
中科院分区：
文献类型：
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作者：
Jinfei Liu;Qiongqiong Lin;Jiayao Zhang;Kui Ren;Jian Lou;Junxu Liu;Li Xiong;J. Pei;Jimeng Sun
数据驱动的机器学习（ML）在各种应用领域取得了巨大成功。由于ML模型训练依赖于大量数据，因此对ML模型训练收集的高质量数据的需求日益增长。数据市场可以用来大大促进数据收集。在这项工作中，我们演示了Dealer，一个具有不同优先级的端到端模型。Dealer由三个实体组成：数据所有者、经纪人和模型购买者。数据所有者获得经纪人分配的数据使用补偿;经纪人从数据所有者那里收集数据，构建模型并将其出售给模型购买者;模型购买者从经纪人那里购买他们的目标模型。我们展示了三个参与实体的功能以及它们之间的简短交互。演示让观众了解和互动体验模型交易的过程。受众可以作为数据所有者来控制数据的补偿内容和补偿方式，可以作为经纪人为机器学习模型定价以获得最大收益，也可以作为模型购买者来购买符合预期的目标模型。
Data-driven machine learning (ML) has witnessed great success across a variety of application domains. Since ML model training relies on a large amount of data, there is a growing demand for high-quality data to be collected for ML model training. Data markets can be employed to significantly facilitate data collection. In this work, we demonstrate Dealer, an en D -to-end mod e l m a rketp l ace with diff e rential p r ivacy. Dealer consists of three entities, data owners, the broker, and model buyers. Data owners receive compensation for their data usages allocated by the broker; The broker collects data from data owners, builds and sells models to model buyers; Model buyers buy their target models from the broker. We demonstrate the functionalities of the three participating entities and the abbreviated interactions between them. The demonstration allows the audience to understand and experience interactively the process of model trading. The audience can act as a data owner to control what and how the data would be compensated, can act as a broker to price machine learning models with maximum revenue, as well as can act as a model buyer to purchase target models that meet expectations.