Sensitive gazetteer discovery and protection for mobile social media users

With the explosive growth of location-aware devices and global adoption of social network applications, enormous volumes of spatiotemporal data are being produced. These can be perceived as gazetteers that record frequently visited locations, e.g. shopping malls and museums, and potentially more sensitive locations, e.g. an individual's home/work locations. Density-based clustering approaches are generally used for gazetteer discovery. However, existing clustering solutions are inefficient for big data scenarios and often disregard mobility features derived from trajectories data. Further, automated gazetteer discovery applications may cause privacy concerns. In this paper, we propose a sensitive gazetteer automated discovery approach based on Ω-cluster with robust privacy controls. The approach identifies sensitive gazetteers from massive trajectory data, with location entropy-based filtering used to reduce the number of uninteresting clusters whilst considering mobility features of trajectories. A parallelized solution is implemented to scale across the cloud using memory-oriented data processing solutions based upon Apache Spark. We embed this algorithm in a privacy-preserving mechanism and subsequently release sanitized gazetteers. Through extensive experiments using synthetic and real trajectory datasets from the location based social network (Twitter), we demonstrate the effectiveness and efficiency of our approach.