Location Privacy in Mobile Big Data: User Identifiability via Habitat Region Representation

Abstract

Mobile big data collected by mobile network operators is of interest to many research communities and industries for its remarkable values. However, such spatiotemporal information may lead to a harsh threat to subscribers’ privacy. This work focuses on subscriber privacy vulnerability assessment in terms of user identifiability across two datasets with significant detail reduced mobility representation. In this paper, we propose an innovative semantic spatiotemporal representation for each subscriber based on the geographic information, termed as daily habitat region, to approximate the subscriber’s daily mobility coverage with far lesser information compared with original mobility traces. The daily habitat region is realized via convex hull extraction on the user’s daily spatiotemporal traces. As a result, user identification can be formulated to match two records with the maximum similarity score between two convex hull sets, obtained by our proposed similarity measures based on cosine distance and permutation hypothesis test. Experiments are conducted to evaluate our proposed innovative mobility representation and user identification algorithms, which also demonstrate that the subscriber’s mobile privacy is under a severe threat even with significantly reduced spatiotemporal information.