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Data Privacy
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Volume 6 Issue 1


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


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.

* Corresponding author.

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ISSN: 1888-5063; ISSN (Digital): 2013-1631; D.L.:B-11873-2008; Web Site: http://www.tdp.cat/
Contact: Transactions on Data Privacy; Vicenç Torra; U. of Skövde; PO Box 408; 54128 Skövde; (Sweden); e-mail:tdp@tdp.cat

 


Vicenç Torra, Last modified: 10 : 35 June 27 2015.