Neuroimaging Based Diagnosis of Alzheimer’s Disease Using Privacy-Preserving Machine Learning (Part 1)
-Authors: Prof. Bogdan Draganski (Director, LREN), Dr. Ferath Kherif (Vice Director, LREN), Claudio Calvaruso (Technical Account Manager, Inpher) and Dr. Dimitar Jetchev (CTO, Inpher) Editors Note: This is the first in a series of articles that LREN – CHUV and Inpher are jointly publishing to show how privacy-preserving…
Building Privacy-Preserving Decision Tree Models Using Multi-Party Computation
-This blog post was jointly written by Dr. Marc Desgroseilliers (Senior Statistics Engineer), Abson Sae-Tang (Director of Engineering) and Dr. Dimitar Jetchev (Co-Founder and CTO) TL;DR Inpher’s team has built XORBoost, an algorithm that enables training an XGBoost model collaboratively on encrypted data.…
Privacy Challenges in Extreme Gradient Boosting
-Editor’s note: This article was originally published on Amazon Science Blog by Dr. Dimitar Jetchev, CTO of Inpher and Joan Feigenbaum, Amazon Scholar and the Grace Hopper Professor of Computer Science at Yale University Machine learning (ML) is increasingly important in a wide range of applications, including market forecasting, service personalization, voice and facial recognition, autonomous driving, health diagnostics, education,…
