  One of the most effective approaches to improving the performance of a
machine-learning model is to acquire additional training data. To do so, a
model owner may seek to acquire relevant training data from a data owner.
Before procuring the data, the model owner needs to appraise the data. However,
the data owner generally does not want to share the data until after an
agreement is reached. The resulting Catch-22 prevents efficient data markets
from forming. To address this problem, we develop data appraisal methods that
do not require data sharing by using secure multi-party computation.
Specifically, we study methods that: (1) compute parameter gradient norms, (2)
perform model fine-tuning, and (3) compute influence functions. Our experiments
show that influence functions provide an appealing trade-off between
high-quality appraisal and required computation.
