Privacy and utility preservation for location data using stay region analysis

Abstract

Location data is very useful for providing location-based services to its users. But the problem in releasing this type of data is that sensitive and private information about users may be leaked. In [11] it is stated that even four spatio-temporal points are enough to uniquely identify 95% of the individuals. There are different approaches for privacy preservation of spatio-temporal data. But in most of these approaches, the utility is severely curtailed in order to ensure risk-free release of data. Our method made a few innovations to retain more utility while not compromising privacy. First of all, for each user we extracted stay regions which are places where a user spends significant amount of time. Then we extracted trajectories or trips between these stay regions. Now the whole data of very big trajectories is converted to trips where each trip has start and end times, and start and end lat-lons. We used these four dimensions in a round robin manner to k-anonymize each trip. We proposed two measures for estimating risk and utility. A nice feature of our method is visualization of k-anonymized trips which can give much better information about mass mobility within a city or area.