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In this paper, we present RedCASTLE, a practically applicable solution forEdge-based $k_s$-anonymization of IoT streaming data in Node-RED. RedCASTLEbuilds upon a pre-existing, rudimentary implementation of the CASTLE algorithmand significantly extends it with functionalities indispensable for real-worldIoT scenarios. In addition, RedCASTLE provides an abstraction layer forsmoothly integrating $k_s$-anonymization into Node-RED, a visually programmablemiddleware for streaming dataflows widely used in Edge-based IoT scenarios.Last but not least, RedCASTLE also provides further capabilities for basicinformation reduction that complement $k_s$-anonymization in theprivacy-friendly implementation of usecases involving IoT streaming data. Apreliminary performance assessment finds that RedCASTLE comes with reasonableoverheads and demonstrates its practical viability.

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