Data Appraisal Without Data Sharing
This work aims to facilitate the formation of open data markets by enabling model owners to appraise data from data owners without privacy violations, which is a significant problem for organizations seeking to improve ML models with external data.
The paper addresses the challenge of appraising data value without direct data sharing, which hinders efficient data markets. It proposes a multi-party computation approach using forward influence functions to approximate data value based on first-order loss reduction, enabling data selection and transaction without sharing private data.
One of the most effective approaches to improving the performance of a machine learning model is to procure additional training data. A model owner seeking relevant training data from a data owner needs to appraise the data before acquiring it. However, without a formal agreement, the data owner does not want to share data. The resulting Catch-22 prevents efficient data markets from forming. This paper proposes adding a data appraisal stage that requires no data sharing between data owners and model owners. Specifically, we use multi-party computation to implement an appraisal function computed on private data. The appraised value serves as a guide to facilitate data selection and transaction. We propose an efficient data appraisal method based on forward influence functions that approximates data value through its first-order loss reduction on the current model. The method requires no additional hyper-parameters or re-training. We show that in private, forward influence functions provide an appealing trade-off between high quality appraisal and required computation, in spite of label noise, class imbalance, and missing data. Our work seeks to inspire an open market that incentivizes efficient, equitable exchange of domain-specific training data.