Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning
Harsh Chaudhari, Rahul Rachuri, Ajith Suresh
Abstract
Machine learning has started to be deployed in fields such as healthcare and finance, which involves dealing with a lot of sensitive data. This propelled the need for and growth of privacy-preserving machine learning (PPML). We propose an actively secure four-party protocol (4PC), and a framework for PPML, showcasing its applications on four of the most widely-known machine learning algorithms -Linear Regression, Logistic Regression, Neural Networks, and Convolutional Neural Networks.
Topics & Concepts
Artificial intelligenceComputer scienceMachine learningConvolutional neural networkProtocol (science)Artificial neural networkOnline machine learningField (mathematics)Active learning (machine learning)Computational learning theoryInstance-based learningFeature (linguistics)Deep learningKey (lock)Support vector machineLearning classifier systemFeature learningAdversarial machine learningInformation privacyCryptography and Data SecurityPrivacy-Preserving Technologies in DataBlockchain Technology Applications and Security