Fundamental Technologies for Machine Learning Centric Data Trading

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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.