Neuroimaging Based Diagnosis of Alzheimer’s Disease Using Privacy-Preserving Machine Learning (Part 2)
-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 second in a series of articles that LREN – CHUV and Inpher are jointly publishing to…
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…
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…
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,…
How to Build Machine Learning Models with Private Data Sources on AWS
-This article was originally published on AWS blogs by Conor Moran, Sr. Director, Business Development, Inpher.
AWS users occasionally need to perform analysis on data sources containing private or sensitive inputs. Inpher’s XOR Secret Computing Platform, available in AWS Marketplace, enables data scientists to train and run machine learning models while maintaining data privacy and without trading utility. Data analysis and machine learning performed by XOR can improve model performance with mathematically guaranteed data privacy while ensuring the data never leaves the data source.
