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

Machine Learning 2022-11-22 v2 Cryptography and Security Software Engineering

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.

Keywords

Cite

@article{arxiv.2211.01827,
  title  = {Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework},
  author = {Ioannis Mavromatis and Aftab Khan},
  journal= {arXiv preprint arXiv:2211.01827},
  year   = {2022}
}

Comments

IEEE CCNC 2023, Las Vegas, USA

R2 v1 2026-06-28T05:06:14.163Z