Delineating social network data anonymization via random edge perturbation

Handle URI:
http://hdl.handle.net/10754/564506
Title:
Delineating social network data anonymization via random edge perturbation
Authors:
Xue, Mingqiang; Karras, Panagiotis; Raïssi, Chedy; Kalnis, Panos ( 0000-0002-5060-1360 ) ; Pung, Hungkeng
Abstract:
Social network data analysis raises concerns about the privacy of related entities or individuals. To address this issue, organizations can publish data after simply replacing the identities of individuals with pseudonyms, leaving the overall structure of the social network unchanged. However, it has been shown that attacks based on structural identification (e.g., a walk-based attack) enable an adversary to re-identify selected individuals in an anonymized network. In this paper we explore the capacity of techniques based on random edge perturbation to thwart such attacks. We theoretically establish that any kind of structural identification attack can effectively be prevented using random edge perturbation and show that, surprisingly, important properties of the whole network, as well as of subgraphs thereof, can be accurately calculated and hence data analysis tasks performed on the perturbed data, given that the legitimate data recipient knows the perturbation probability as well. Yet we also examine ways to enhance the walk-based attack, proposing a variant we call probabilistic attack. Nevertheless, we demonstrate that such probabilistic attacks can also be prevented under sufficient perturbation. Eventually, we conduct a thorough theoretical study of the probability of success of any}structural attack as a function of the perturbation probability. Our analysis provides a powerful tool for delineating the identification risk of perturbed social network data; our extensive experiments with synthetic and real datasets confirm our expectations. © 2012 ACM.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Publisher:
Association for Computing Machinery (ACM)
Journal:
Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12
Conference/Event name:
21st ACM International Conference on Information and Knowledge Management, CIKM 2012
Issue Date:
2012
DOI:
10.1145/2396761.2396823
Type:
Conference Paper
ISBN:
9781450311564
Appears in Collections:
Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorXue, Mingqiangen
dc.contributor.authorKarras, Panagiotisen
dc.contributor.authorRaïssi, Chedyen
dc.contributor.authorKalnis, Panosen
dc.contributor.authorPung, Hungkengen
dc.date.accessioned2015-08-04T07:02:44Zen
dc.date.available2015-08-04T07:02:44Zen
dc.date.issued2012en
dc.identifier.isbn9781450311564en
dc.identifier.doi10.1145/2396761.2396823en
dc.identifier.urihttp://hdl.handle.net/10754/564506en
dc.description.abstractSocial network data analysis raises concerns about the privacy of related entities or individuals. To address this issue, organizations can publish data after simply replacing the identities of individuals with pseudonyms, leaving the overall structure of the social network unchanged. However, it has been shown that attacks based on structural identification (e.g., a walk-based attack) enable an adversary to re-identify selected individuals in an anonymized network. In this paper we explore the capacity of techniques based on random edge perturbation to thwart such attacks. We theoretically establish that any kind of structural identification attack can effectively be prevented using random edge perturbation and show that, surprisingly, important properties of the whole network, as well as of subgraphs thereof, can be accurately calculated and hence data analysis tasks performed on the perturbed data, given that the legitimate data recipient knows the perturbation probability as well. Yet we also examine ways to enhance the walk-based attack, proposing a variant we call probabilistic attack. Nevertheless, we demonstrate that such probabilistic attacks can also be prevented under sufficient perturbation. Eventually, we conduct a thorough theoretical study of the probability of success of any}structural attack as a function of the perturbation probability. Our analysis provides a powerful tool for delineating the identification risk of perturbed social network data; our extensive experiments with synthetic and real datasets confirm our expectations. © 2012 ACM.en
dc.publisherAssociation for Computing Machinery (ACM)en
dc.subjectgraph utilityen
dc.subjectprivacyen
dc.subjectrandom perturbationen
dc.subjectsocial networken
dc.titleDelineating social network data anonymization via random edge perturbationen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journalProceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12en
dc.conference.date29 October 2012 through 2 November 2012en
dc.conference.name21st ACM International Conference on Information and Knowledge Management, CIKM 2012en
dc.conference.locationMaui, HIen
dc.contributor.institutionI2Research, Singapore, Singaporeen
dc.contributor.institutionRutgers University, Newark, NJ, United Statesen
dc.contributor.institutionINRIA Nancy, Nancy, Franceen
dc.contributor.institutionNUS, Singapore, Singaporeen
kaust.authorKalnis, Panosen
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