Towards a Privacy preserving Machine Learning based Access Control for the Internet of Things

Abstract

The Internet of Things (IoT) is pushing us towards adopting original security approaches to manage the huge amount of data they produce while respecting its computing and storage constraints. For that matter, this paper endorses getting the most out of machine learning (ML) algorithms as a means to build smart and dynamic Access Control (AC) solutions for the IoT. Unfortunately, ML requires abundant amount of data for training, whereas a large part of the data exchanged by IoT devices is private, hence this paper is offering a way out to reconcile these two concepts through inter-organizational differential privacy. The proposed solution is backed up by an implementation and shows promising results compared to popular AC models and standards.