Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework
It addresses data integrity and confidentiality issues for IoT applications, but appears incremental as it builds on existing drift detection mechanisms.
The paper tackles data drift detection in IoT sensor deployments by introducing LE3D, a privacy-preserving and lightweight framework that operates distributively and supports multiple drift estimators for time-series data, with results demonstrated in a real-world-like scenario.
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.