RedCASTLE: Practically Applicable $k_s$-Anonymity for IoT Streaming Data at the Edge in Node-RED
This work addresses privacy concerns for IoT data streams at the edge, but it is incremental as it builds upon an existing algorithm.
The paper tackles the problem of anonymizing IoT streaming data at the edge by presenting RedCASTLE, a solution that extends the CASTLE algorithm for practical use in Node-RED, with a preliminary performance assessment showing reasonable overheads.
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.