On the Privacy Implications of Location Semantics

Abstract

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.

Rethinking Location Privacy for Unknown Mobility Behaviors

“…Second, hardwired user mobility models are used to obtain utility improvements and derive optimal geo-indistinguishability LPPMs [2], or to evaluate a semantic variation of this notion [3]. Non-sporadic location privacy works also hardwired their mobility models on the evaluation data, and typically adopt a Markov model for user mobility to account for temporal correlations [4], [7].…”

Pets without PETs: on pet owners’ under-estimation of privacy concerns in pet wearables

“…Field studies examining the public use of smart glasses revealed that bystanders expressed interest in being asked permission before being recorded, as well as desiring technology to block being recorded [23]. Ağır et al [5] 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.…”

PARTS – Privacy-Aware Routing with Transportation Subgraphs

“…recommendation of restaurants nearby) or to share information about a visited venue, Agir et al [1] present a solution where the semantic dimension of a location can be protected by generalising the semantic tag and locations can be obfuscated.…”