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dc.contributor.authorXue, Mingqiang
dc.contributor.authorKarras, Panagiotis
dc.contributor.authorRaïssi, Chedy
dc.contributor.authorKalnis, Panos
dc.contributor.authorPung, Hungkeng
dc.date.accessioned2015-08-04T07:02:44Z
dc.date.available2015-08-04T07:02:44Z
dc.date.issued2012
dc.identifier.citationXue, M., Karras, P., Chedy, R., Kalnis, P., & Pung, H. K. (2012). Delineating social network data anonymization via random edge perturbation. Proceedings of the 21st ACM International Conference on Information and Knowledge Management - CIKM ’12. doi:10.1145/2396761.2396823
dc.identifier.isbn9781450311564
dc.identifier.doi10.1145/2396761.2396823
dc.identifier.urihttp://hdl.handle.net/10754/564506
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.
dc.publisherAssociation for Computing Machinery (ACM)
dc.subjectgraph utility
dc.subjectprivacy
dc.subjectrandom perturbation
dc.subjectsocial network
dc.titleDelineating social network data anonymization via random edge perturbation
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journalProceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12
dc.conference.date29 October 2012 through 2 November 2012
dc.conference.name21st ACM International Conference on Information and Knowledge Management, CIKM 2012
dc.conference.locationMaui, HI
dc.contributor.institutionI2Research, Singapore, Singapore
dc.contributor.institutionRutgers University, Newark, NJ, United States
dc.contributor.institutionINRIA Nancy, Nancy, France
dc.contributor.institutionNUS, Singapore, Singapore
kaust.personKalnis, Panos


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