Hawk: Accurate and Fast Privacy-Preserving Machine Learning Using Secure Lookup Table Computation

arXiv:2403.17296v1 Announce Type: cross
Abstract: Training machine learning models on data from multiple entities without direct data sharing can unlock applications otherwise hindered by business, legal, or ethical constraints. In this work, we design and implement new privacy-preserving machine learning protocols for logistic regression and neural network models. We adopt a two-server model where data owners secret-share their data between two servers that train and evaluate the model on the joint data. A significant source of inefficiency and inaccuracy in …

abstract applications arxiv business computation constraints cs.cr cs.lg data data sharing design ethical legal logistic regression machine machine learning machine learning models multiple network neural network privacy regression table training type work