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.

翻译：在不受约束的数据市场中，关键在于能够在数据所有者与模型所有者最终完成交易之前，谨慎地选择并评估训练数据。为保护数据和模型的隐私，这一过程涉及通过多方计算（MPC）对目标模型进行审查。尽管先前的研究认为基于MPC的Transformer模型评估资源消耗过高，但本文引入了一种创新的方法，使得数据选择变得切实可行。本研究的贡献包含三个关键要素：（1）一种利用MPC进行机密数据选择的开创性流程；（2）使用在有限相关数据子集上训练的简化低维多层感知器（MLP）来复制复杂的高维操作；（3）以并发、多阶段的方式实现MPC。所提出的方法在一系列Transformer模型和NLP/CV基准测试中进行了评估。与直接基于MPC的目标模型评估相比，我们的方法将所需时间从数千小时大幅缩短至仅数十小时，同时在使用所选数据训练时，准确率仅下降0.20%。