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

Abstract

The Internet of Things (IoT) has formed a whole new layer of the world built on the Internet, reaching every connected device, actuator, and sensor. 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 provide 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, the minimization of missingness has to be considered for the varied data stream anonymization. Therefore, we propose the 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 data sets shows that X-BAND is effective and efficient compared to the famous conventional anonymization algorithm FADS. X-BAND demonstrated 5%-11% and 1%-3% less information loss on real data sets Adult and PM2.5, respectively, while performing similar on clustering, comparable to reusing suppression and runtime. Also, the new weighted distance function is effective for reducing missingness for anonymization.