Mobile users increasingly make use of location-based online services enabled by localization systems. Not only do they share their locations to obtain contextual services in return (e.g., 'nearest restaurant'), but they also share, with their friends, information about the venues (e.g., the type, such as a restaurant or a cinema) they visit. This introduces an additional dimension to the threat to location privacy: location semantics, combined with location information, can be used to improve location inference by learning and exploiting patterns at the semantic level (e.g., people go to cinemas after going to restaurants). Conversely, the type of the venue a user visits can be inferred, which also threatens her semantic location privacy. In this paper, we formalize this problem and analyze the effect of venue-type information on location privacy. We introduce inference models that consider location semantics and semantic privacy-protection mechanisms and evaluate them by using datasets of semantic check-ins from Foursquare, totaling more than a thousand users in six large cities. Our experimental results show that there is a significant risk for users' semantic location privacy and that semantic information improves inference of user locations.
Ağır et al. REF discussed the threat of location data being linked with its semantics as a way of learning about people's behavior (e.g., people go to cinemas after going to restaurants), experimentally showing significant risk for users' semantic location privacy.
On the Privacy Implications of Location Semantics
Proceedings on Privacy Enhancing Technologies
Computer Science