A reciprocal framework for spatial K-anonymity

Handle URI:
http://hdl.handle.net/10754/561439
Title:
A reciprocal framework for spatial K-anonymity
Authors:
Ghinita, Gabriel; Zhao, Keliang; Papadias, Dimitris; Kalnis, Panos ( 0000-0002-5060-1360 )
Abstract:
Spatial K-anonymity (SKA) exploits the concept of K-anonymity in order to protect the identity of users from location-based attacks. The main idea of SKA is to replace the exact location of a user U with an anonymizing spatial region (ASR) that contains at least K-1 other users, so that an attacker can pinpoint U with probability at most 1/K. Simply generating an ASR that includes K users does not guarantee SKA. Previous work defined the reciprocity property as a sufficient condition for SKA. However, the only existing reciprocal method, Hilbert Cloak, relies on a specialized data structure. In contrast, we propose a general framework for implementing reciprocal algorithms using any existing spatial index on the user locations. We discuss ASR construction methods with different tradeoffs on effectiveness (i.e., ASR size) and efficiency (i.e., construction cost). Then, we present case studies of applying our framework on top of two popular spatial indices (namely, R*-trees and Quad-trees). Finally, we consider the case where the attacker knows the query patterns of each user. The experimental results verify that our methods outperform Hilbert Cloak. Moreover, since we employ general-purpose spatial indices, the proposed system is not limited to anonymization, but supports conventional spatial queries as well. © 2009 Elsevier B.V. All rights reserved.
KAUST Department:
Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
Elsevier
Journal:
Information Systems
Issue Date:
May-2010
DOI:
10.1016/j.is.2009.10.001
Type:
Article
ISSN:
03064379
Appears in Collections:
Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorGhinita, Gabrielen
dc.contributor.authorZhao, Keliangen
dc.contributor.authorPapadias, Dimitrisen
dc.contributor.authorKalnis, Panosen
dc.date.accessioned2015-08-02T09:11:20Zen
dc.date.available2015-08-02T09:11:20Zen
dc.date.issued2010-05en
dc.identifier.issn03064379en
dc.identifier.doi10.1016/j.is.2009.10.001en
dc.identifier.urihttp://hdl.handle.net/10754/561439en
dc.description.abstractSpatial K-anonymity (SKA) exploits the concept of K-anonymity in order to protect the identity of users from location-based attacks. The main idea of SKA is to replace the exact location of a user U with an anonymizing spatial region (ASR) that contains at least K-1 other users, so that an attacker can pinpoint U with probability at most 1/K. Simply generating an ASR that includes K users does not guarantee SKA. Previous work defined the reciprocity property as a sufficient condition for SKA. However, the only existing reciprocal method, Hilbert Cloak, relies on a specialized data structure. In contrast, we propose a general framework for implementing reciprocal algorithms using any existing spatial index on the user locations. We discuss ASR construction methods with different tradeoffs on effectiveness (i.e., ASR size) and efficiency (i.e., construction cost). Then, we present case studies of applying our framework on top of two popular spatial indices (namely, R*-trees and Quad-trees). Finally, we consider the case where the attacker knows the query patterns of each user. The experimental results verify that our methods outperform Hilbert Cloak. Moreover, since we employ general-purpose spatial indices, the proposed system is not limited to anonymization, but supports conventional spatial queries as well. © 2009 Elsevier B.V. All rights reserved.en
dc.publisherElsevieren
dc.subjectAnonymityen
dc.subjectLocation-based servicesen
dc.subjectPrivacyen
dc.subjectSpatial databasesen
dc.titleA reciprocal framework for spatial K-anonymityen
dc.typeArticleen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalInformation Systemsen
dc.contributor.institutionDepartment of Computer Science, Purdue University, United Statesen
dc.contributor.institutionDepartment of Computer Science and Engineering, University of California at San Diego, United Statesen
dc.contributor.institutionDepartment of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kongen
kaust.authorKalnis, Panosen
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