Data Appraisal Without Data Sharing

TL;DR

This paper introduces a privacy-preserving data appraisal method using multi-party computation and influence functions to facilitate data exchange without sharing raw data, aiming to improve data markets.

Contribution

It proposes a novel data appraisal approach that estimates data value privately via influence functions, enabling efficient data transactions without data sharing.

Findings

Effective data valuation despite label noise and class imbalance

No additional hyper-parameters or re-training needed

Balances appraisal quality with computational efficiency

Abstract

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.