RedCASTLE: Practically Applicable $k_s$-Anonymity for IoT Streaming Data at the Edge in Node-RED

TL;DR

RedCASTLE offers a practical, extendable solution for $k_s$-anonymization of IoT streaming data at the edge, integrating seamlessly with Node-RED and supporting privacy-preserving data processing.

Contribution

It introduces RedCASTLE, an enhanced, real-world applicable implementation of $k_s$-anonymity for IoT data in Node-RED, with added functionalities and integration capabilities.

Findings

RedCASTLE has reasonable performance overheads.

It demonstrates practical viability in IoT edge scenarios.

Provides effective privacy-preserving data anonymization.

Abstract

In this paper, we present RedCASTLE, a practically applicable solution for Edge-based $k_s$-anonymization of IoT streaming data in Node-RED. RedCASTLE builds upon a pre-existing, rudimentary implementation of the CASTLE algorithm and significantly extends it with functionalities indispensable for real-world IoT scenarios. In addition, RedCASTLE provides an abstraction layer for smoothly integrating $k_s$-anonymization into Node-RED, a visually programmable middleware for streaming dataflows widely used in Edge-based IoT scenarios. Last but not least, RedCASTLE also provides further capabilities for basic information reduction that complement $k_s$-anonymization in the privacy-friendly implementation of usecases involving IoT streaming data. A preliminary performance assessment finds that RedCASTLE comes with reasonable overheads and demonstrates its practical viability.