Demonstration of dealer: An end-to-end model marketplace with differential privacy

Qiongqiong Lin, Jiayao Zhang, Jinfei Liu, Kui Ren, Jian Lou, Junxu Liu, Li Xiong, Jian Pei, Jimeng Sun

Research output: Contribution to journalConference articlepeer-review


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 enD-to-end model marketplace with differential privacy. 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.

Original languageEnglish (US)
Pages (from-to)2747-2750
Number of pages4
JournalProceedings of the VLDB Endowment
Issue number12
StatePublished - 2021
Event47th International Conference on Very Large Data Bases, VLDB 2021 - Virtual, Online
Duration: Aug 16 2021Aug 20 2021

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • General Computer Science


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