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

arXiv:2403.17296v1 Announce Type: new
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 …

applications arxiv business can computation constraints cs.cr cs.lg data data sharing design entities ethical fast legal machine machine learning machine learning models network neural network privacy protocols sharing training unlock work