论文标题
演示：LE3D：保存隐私的轻巧数据漂移检测框架
Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework
论文作者
论文摘要
本文介绍了LE3D；一个新型的数据漂移检测框架，用于保留数据完整性和机密性。 LE3D是一个可普遍的平台，用于评估物联网（IoT）传感器部署中新型漂移检测机制。我们的框架以分布式方式运行，保留数据隐私，同时仍然适应以最少的在线重新配置的新传感器。我们的框架目前支持时间序列IoT数据的多个漂移估计器，并且可以轻松扩展以适应新的数据类型和漂移检测机制。该演示将说明在类似于现实世界的情况下LE3D的功能。
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.
