Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework

TL;DR

LE3D is a privacy-preserving, adaptable, and distributed framework for detecting data drift in IoT sensor data, supporting multiple estimators and easy extension to new data types.

Contribution

The paper introduces LE3D, a novel framework that enables privacy-preserving, distributed data drift detection in IoT environments with minimal reconfiguration.

Findings

Supports multiple drift estimators for IoT data

Operates in a distributed manner preserving data privacy

Easily extendable to new data types and drift detection mechanisms

Abstract

This paper presents LE3D; a novel data drift detection framework for preserving data integrity and confidentiality. LE3D is a generalisable platform for evaluating novel drift detection mechanisms within the Internet of Things (IoT) sensor deployments. Our framework operates in a distributed manner, preserving data privacy while still being adaptable to new sensors with minimal online reconfiguration. Our framework currently supports multiple drift estimators for time-series IoT data and can easily be extended to accommodate new data types and drift detection mechanisms. This demo will illustrate the functionality of LE3D under a real-world-like scenario.