Privacy-protected place of activity mining on big location data
Shuo Wang, Richard Sinnott, Surya Nepal · 2017
People always spend their time at a few important locations for various activities in groups during specific time slots, called place of activity (POA), e.g., resting at home among family members during night and working at office among colleagues during work time. Inferring such places is significant for not only the precise advertising on the commercial aspect but the identifying rallies or meetings among a group of people and tracking of the target individuals on the aspect of public security, e.g., locating and tracking suspected terrorists for anti-terrorist work. However, it is a challenge to map from big location data to places of activity due to the volume and complexity whilst giving rise to privacy concerns, e.g., personally important place mining. In the paper, a method for POA mining on big location data is proposed, named P-PAM, aiming at big data analytics and privacy concerns. We use a clustering algorithm to discover the place of activity, then adopt location entropy as reference of user diversity and take into account temporal variation, to infer place of activity. Further, robust privacy-preserving mechanisms under differential privacy are embedded into clustering results and location entropy evaluation that accesses to raw location data. We demonstrate the utility of our proposed approach with large-scale location datasets derived from geo-referenced social media. The experimental results suggest that the POA mining approach can successfully scale to big data scenarios whilst preserving individual user privacy.