Distributed Variational Information Bottleneck for IOT Environments

Abstract

Deep learning is becoming the latest trend in sensitive applications, such as healthcare, criminal justice, and finance. As these new applications emerge, adversaries are developing ways to circumvent them. In this paper, we investigate users' revealing data to the public; parts of it are often sensitive when compactly represented. The representation should ensure that the target information is served accurately and reliably while simultaneously safeguarding sensitive information. In order to achieve that goal, we present a supervised deep learning framework based on the Information Bottleneck (IB) principle. The purpose of this was to maximize the mutual information between utility labels, and the learned compressing representation while minimizing the mutual information between the learned compressing representation and the original representation. Additionally, we examine a distributed learning framework to securely aggregate data from the Internet of Things (IoT) devices and create a utility model that is compatible with IoT devices. We apply the variational mutual information approximation to gain an accurate representation of bottlenecks. Through experiments with synthetic datasets, we demonstrate the efficiency and privacy-preserving capabilities of our framework.