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dc.contributor.authorTian, Hui
dc.contributor.authorLiu, Jingtian
dc.contributor.authorShen, Hong
dc.date.accessioned2020-03-25T04:10:39Z
dc.date.available2020-03-25T04:10:39Z
dc.date.issued2018
dc.identifier.issn0045-7906
dc.identifier.doi10.1016/j.compeleceng.2017.12.003
dc.identifier.urihttp://hdl.handle.net/10072/392619
dc.description.abstractData in social networking services (SNS) are spreading widely at high speed and always have significant impacts. It is very vulnerable to attacks. Many segments of information can be defined as user’s privacy. In a friends’ network, subgraph structure of nodes could be the basis for an adversary identifying the target node. In a computer network, a link’s weight between two nodes represents how often they communicate between each other. Attackers would collect these personal data and analyze them by various data mining methods. There are mainly two categories of targets, node re-identification, and linkage information. In order to protect different kinds of sensitive private information, it is demanding to establish appropriate privacy preserving models and develop efficient methods to protect private data before publishing. In many existing models, node degree and subgraph information are the sensitive information which may be used by adversary parties in node/linkage disclosure. They assumed the adversary has partial information from open queries. Different techniques have been applied in privacy preserving before publishing graph data, such as dynamic programming and forming super-node to guarantee k-anonymity and l-diversity [23,24] and differential privacy based techniques [1,12,13]. However, these methods are either identified as NP-hard problems or convergent to locally optimized solution. The data utility is low after publishing.
dc.description.peerreviewedYes
dc.languageen
dc.publisherElsevier BV
dc.relation.ispartofpagefrom415
dc.relation.ispartofpageto424
dc.relation.ispartofjournalComputers and Electrical Engineering
dc.relation.ispartofvolume67
dc.subject.fieldofresearchComputer Software
dc.subject.fieldofresearchDistributed Computing
dc.subject.fieldofresearchElectrical and Electronic Engineering
dc.subject.fieldofresearchcode0803
dc.subject.fieldofresearchcode0805
dc.subject.fieldofresearchcode0906
dc.titleDiffusion Wavelet-based Privacy Preserving in Social Networks
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationTian, H; Liu, J; Shen, H, Diffusion Wavelet-based Privacy Preserving in Social Networks, Computers and Electrical Engineering, 2018, 67, pp. 415-424
dc.date.updated2020-03-25T02:52:09Z
gro.hasfulltextNo Full Text
gro.griffith.authorTian, Hui


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