Toward Anonymizing IoT Data Streams via Partitioning

Internet-of-Things (IoT) devices are capable of capturing physiological measures, location and activity information, hence sharing sensed data can lead to privacy implications. Data anonymization provides solution to this problem, however, traditional anonymization approaches only provide privacy protection for data stream generated from a single entity. Since, a single entity can make use of multiple IoT devices at an instance, IoT data streams are not fixed in nature. As conventional data stream anonymization algorithms only work on fixed width data stream they cannot be applied to IoT. In this work, we propose an anonymization algorithm for publishing IoT data streams. Our approach anonymizes tuples with similar description in a single cluster under time based sliding window. It considers similarity of tuples when clustering, and provides solution to anonymize tuples with missing values using representative values. Our experiment on real dataset shows that the proposed algorithm publishes data with less information loss and runs faster compared to conventional anonymization approaches modified to run for IoT data streams.