EE Seminar: Private Collaborative Learning of Intelligible Models on Vertically Split Data
(The talk will be given in English)
Speaker: Dr. Ran Gilad-Bachrach
Microsoft
Monday, May 7th, 2018
15:00 - 16:00
Room 011, Kitot Bldg., Faculty of Engineering
Private Collaborative Learning of Intelligible Models on Vertically Split Data
Abstract
A common privacy scenario in machine learning is one in which possibly several different parties hold fragments of a training dataset, and want to train a model together without revealing any unnecessary information to each other about their data. We present methods for training intelligible models, namely Generalized Additive Models (GAMs), on datasets that are vertically split across multiple parties. Our protocol uses fully homomorphic encryption to guarantee data privacy, and is proved to be secure in the semi-honest security model under mild non-collusion assumptions. We prove the privacy properties of our solution as well as demonstrate its practicality in empirical evaluation.
Bio
Ran Gilad-Bachrach is a researcher at Microsoft Research. He studies multiple aspects of machine learning, currently he focuses on Private-AI and Precision-Psychology. Private-AI is the study of privacy preserving and collaboration enabling machine learning. Precision-Psychology is the study of the applications of data science to improving outcomes in helping people going through behavioral changes or fighting disorders. Ran earned his Ph.D. from the Hebrew University of Jerusalem and worked at Intel Research and Bing before joining Microsoft Research.
