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 appropriate 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. First, we 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 further check the quality of the selected data. Compared to random data selection, the experimental results show that our solution can reduce 60% prediction errors.

基于云的数据市场提供一种服务，将数据购买者与合适的数据卖家进行匹配，以便数据购买者能够用外部数据扩充其内部数据集，从而改进他们的机器学习（ML）模型。由于数据可能包含不同的值，对于购买者来说，在最终交易之前评估最有价值的数据至关重要。然而，评估ML数据通常需要云访问购买者的ML模型和卖家的数据，而这两者都是敏感的。现有的基于云的数据市场都无法在保护模型隐私和数据隐私的同时进行ML数据评估。在本文中，我们在基于云的数据市场上开发了一个保护隐私的ML数据评估框架，以保护购买者的ML模型和卖家的数据。首先，我们提供一个保护隐私的框架，允许购买者和卖家分别对他们的模型和数据进行加密，同时在云中保留数据功能和模型功能。然后，我们开发了一个保护隐私的数据选择协议，使云能够帮助购买者选择最有价值的ML数据。此外，我们还开发了一个保护隐私的数据验证协议，允许购买者进一步检查所选数据的质量。与随机数据选择相比，实验结果表明我们的解决方案可以减少60%的预测误差。