$X-BAND$ : Expiration Band for Anonymizing Varied Data Streams

Abstract

The Internet-of-Things (IoT) has formed a whole new layer of the world built on internet, reaching every connected devices, actuators and sensors. Many organizations utilize IoT data streams for research and development purposes. To make value out of these data streams, the data handling party must ensure the privacy of the individuals. The most common approach to provide privacy preservation is anonymization. IoT data provides varied data streams due to the nature of the individual's preference and versatile devices pool. The conventional single tuple expiration driven sliding window method is not adequate to provide efficient anonymization. Furthermore, minimization of missingness has to be considered for the varied data stream anonymization. Therefore, we propose X-BAND algorithm that utilizes the new expiration-band mechanism for handling varied data streams to achieve efficient anonymization, and we introduce weighted distance function for X-BAND to reduce missingness of published data. Our experiment on real datasets shows that X-BAND is effective and efficient compared to famous conventional anonymization algorithm FADS. X-BAND demonstrated 5% to 11% and 1% to 3% less information loss on real dataset Adult and PM2.5 respectively while performing similar on clustering, comparable to re-using suppression and runtime. Also, the new weighted distance function is effective for reducing missingness for anonymization.

A Review of Anonymization for Healthcare Data

“…Anonymization for incremental medical data. Medical records are incremental in nature, therefore, methods for incremental anonymization or data streams have been proposed where the goal is to anonymize medical data as they increase [13], [30], [47,82,106], [67], [46], [78], [79]. Such methods are based on accumulation, aggregation, and clustering.…”