Fundamental Technologies for Machine Learning Centric Data Trading

我们在为机器学习（ML）应用建立可靠的数据市场方面取得了重大进展。具体而言，在多个数据所有者合作训练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以鼓励公平合作至关重要。然而，现有的研究忽略了贡献评估过程中潜在的隐私泄露。我们提出了在协作ML中进行隐私保护贡献评估的开创性方法，以解决这一重大限制。我们的方法使买家能够在购买之前评估数据产品的质量，而不会访问产品并牺牲数据所有者的隐私，这大大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.