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On the Privacy Implications of Location Semantics

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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings on Privacy Enhancing Technologies de Gruyter

On the Privacy Implications of Location Semantics

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Publisher
de Gruyter
Copyright
Copyright © 2016 by the
ISSN
2299-0984
eISSN
2299-0984
DOI
10.1515/popets-2016-0034
Publisher site
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Abstract

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.

Journal

Proceedings on Privacy Enhancing Technologies – de Gruyter

Published: Oct 1, 2016

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