  Essential for an unfettered data market is the ability to discreetly select
and evaluate training data before finalizing a transaction between the data
owner and model owner. To safeguard the privacy of both data and model, this
process involves scrutinizing the target model through Multi-Party Computation
(MPC). While prior research has posited that the MPC-based evaluation of
Transformer models is excessively resource-intensive, this paper introduces an
innovative approach that renders data selection practical. The contributions of
this study encompass three pivotal elements: (1) a groundbreaking pipeline for
confidential data selection using MPC, (2) replicating intricate
high-dimensional operations with simplified low-dimensional MLPs trained on a
limited subset of pertinent data, and (3) implementing MPC in a concurrent,
multi-phase manner. The proposed method is assessed across an array of
Transformer models and NLP/CV benchmarks. In comparison to the direct MPC-based
evaluation of the target model, our approach substantially reduces the time
required, from thousands of hours to mere tens of hours, with only a nominal
0.20% dip in accuracy when training with the selected data.
