Anonymizing geo-social network datasets

Authors:
Amirreza Masoumzadeh;James Joshi
Affiliations:
University of Pittsburgh, Pittsburgh, PA;University of Pittsburgh, Pittsburgh, PA
Venue:
Proceedings of the 4th ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS
Year:
2011

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Abstract

Geo-social networking systems, such as Foursquare and Face-book Places, where users perform interactions based on their self-reported locations are growing fast nowadays. The location-rich social network data collected in such systems could be of research interest for various purposes. However, such datasets are at the risk of user re-identification and consequently privacy violation of the involved users if they are not adequately anonymzied. In this paper, we study the problem of anonymizing a geo-social network dataset, based on adversarial knowledge on location information of its users. We introduce k-anonymity-based properties for guaranteeing anonymity based on location information, provide a realistic model of location data in geo-social networks, and propose corresponding anonymization algorithms. We also evaluate the proposed solutions using a synthetic GSN dataset.