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Try before You Buy: Privacy-preserving Data Evaluation on Cloud-based Machine Learning Data Marketplace
A cloud-based data marketplace provides a service to match data shoppers with the right data sellers, so that data shoppers can augment their internal data sets with external data to improve their machine learning (ML) models. Since data may contain diverse values, it is critical for a shopper to evaluate the most valuable data before making the final trade. However, evaluating ML data typically requires the cloud to access a shopper's ML model and sellers' data, which are both sensitive. None of the existing cloud-based data marketplaces enable ML data evaluation while preserving both model privacy and data privacy. In this paper, we develop a privacy-preserving ML data evaluation framework on a cloud-based data marketplace to protect shoppers' ML models and sellers' data. We first provide a privacy-preserving framework that allows shoppers and sellers to encrypt their models and data, respectively, while preserving data functionality and model functionality in the cloud. We then develop a privacy-preserving data selection protocol that enables the cloud to help shoppers select the most valuable ML data. Also, we develop a privacy-preserving data validation protocol that allows shoppers to check the quality of the selected data. Compared to random data selection, the experimental results show that our solution can reduce 60% prediction errors.