Secure and Effective Data Appraisal for Machine Learning. (arXiv:2310.02373v2 [cs.LG] UPDATED)

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 …

computation data data owner machine machine learning market mpc party privacy process safeguard select target training training data transaction