A Novel Approach to Preserving Privacy in Social Network Data Publishing

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dc.contributor.authorBensimessaoud, Sihem
dc.contributor.authorBenmeziane, Souad
dc.contributor.authorBadache, Nadjib
dc.contributor.authorDjellalbia, Amina
dc.date.accessioned2016-10-26T13:38:13Z
dc.date.available2016-10-26T13:38:13Z
dc.date.issued2016-10-24
dc.description.abstractToday, more and more social network data are published for data analysis. Although this analysis is important, these publications may be targeted by re-identification attacks i.e., where an attacker tries to recover the identities of some nodes that were removed during the anonymization process. Among these attacks, we distinguish "the neighborhood attacks" where an attacker can have background knowledge about the neighborhoods of target victims. Researchers have developed anonymization models similar to k-anonymity, based on edge adding, but can significantly alter the properties of the original graph. In this work, a new anonymization algorithm based on the addition of fake nodes is proposed, which ensures that the published graph preserves another important utility that is the average path length “APL”.fr_FR
dc.identifier.isrnCERIST-DTISI/RR--16-000000020--DZfr_FR
dc.identifier.urihttp://dl.cerist.dz/handle/CERIST/855
dc.publisherCERIST
dc.relation.ispartofRapports de recherche internes
dc.relation.placeAlger
dc.structureSécurité des données et vie privéefr_FR
dc.subjectNeighborhood attack; Social network data publication; Anonymization; Utility.fr_FR
dc.titleA Novel Approach to Preserving Privacy in Social Network Data Publishingfr_FR
dc.typeTechnical Report
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