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Top Location Anonymization for Geosocial Network Datasets
Amirreza Masoumzadeh(a),(*), James Joshi(a)
Transactions on Data Privacy 6:1 (2013) 107 - 126
Abstract, PDF
(a) School of Information Sciences, University of Pittsburgh, IS Building, 135 N. Bellefield Ave., Pittsburgh, PA 15260, USA.
e-mail:amirreza @sis.pitt.edu; jjoshi @pitt.edu
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Abstract
Geosocial networks such as Foursquare have access to users' location information, friendships, and other potentially privacy sensitive information. In this paper, we show that an attacker with access to a naively-anonymized geosocial network dataset can breach users' privacy by considering location patterns of the target users. We study the problem of anonymizing such a dataset in order to avoid re-identification of a user based on her or her friends' location information. We introduce
k-anonymity-based properties for geosocial network datasets, propose appropriate data models
and algorithms, and evaluate our approach on both synthetic and real-world datasets.
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