Fundamental Technologies for Machine Learning Centric Data Trading

我们在为机器学习(ML)应用程序建立可靠的数据市场方面取得了重大进展。具体地说，在多个数据所有者协作训练ML模型的协作数据市场中，评估所有者对模型效用的不同贡献是至关重要的，以鼓励公平合作。然而，现有的研究忽略了投稿评估过程中潜在的隐私泄露。针对这一缺陷，我们提出了协作式ML中隐私保护贡献评估的开创性方法。我们的方法使买家能够在购买前评估数据产品的质量，而无需访问产品和牺牲数据所有者的隐私，从而极大地促进了可靠的数据交易。

We have made a significant advance in building a reliable data market for machine learning (ML) applications. Concretely, in a collaborative data marketplace where multiple data owners collaborate to train an ML model, it is essential to evaluate the owners' diverse contributions to the model's utility to encourage fair cooperation. However, existing studies have neglected the potential privacy leakage in the contribution evaluation process. We have proposed pioneering methods for privacy-preserving contribution evaluation in collaborative ML to address this significant limitation. Our methods enable buyers to estimate data products' qualities before purchasing without accessing the products and sacrificing data owners' privacy, which considerably promotes reliable data trading.