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dc.contributor.authorReza, KJ
dc.contributor.authorIslam, MZ
dc.contributor.authorEstivill-Castro, V
dc.date.accessioned2021-04-23T01:06:59Z
dc.date.available2021-04-23T01:06:59Z
dc.date.issued2021
dc.identifier.issn0941-0643
dc.identifier.doi10.1007/s00521-021-05860-8
dc.identifier.urihttp://hdl.handle.net/10072/403928
dc.description.abstractA malicious data miner can infer users’ private information in online social networks (OSNs) by data mining the users’ disclosed information. By exploring the public information about a target user (i.e. an individual or a group of OSN users whose privacy is under attack), attackers can prepare a training data set holding similar information about other users who openly disclosed their data. Using a machine learning classifier, the attacker can input released information about users under attack as non-class attributes and extract the private information as a class attribute. Some techniques offer some privacy protection against specific classifiers;, however, the provided privacy can be under threat if an attacker uses a different classifier (rather than the one used by the privacy protection techniques) to infer sensitive information. In reality, it is difficult to predict the classifiers involved in a privacy attack. In this study, we propose a privacy-preserving technique which first prepares a training data set in a similar way that an attacker can prepare and then takes an approach independent of the classifiers to extract patterns (or logic rules) from the training data set. Based on the extracted rule set, it then suggests the target users to hide some non-class attribute values and/or modify some friendship links for protecting their privacy. We apply our proposed technique on two OSN data sets containing users’ attribute values and their friendship links. For evaluating the performance of the proposed technique, we use conventional classifiers such as Nai ¨ ve Bayes, Support Vector Machine and Random Forest on the privacy-protected data sets. The experimental results show that our proposed technique outperforms the existing privacy-preserving algorithms in terms of securing privacy while maintaining the data utility.
dc.description.peerreviewedYes
dc.languageen
dc.publisherSpringer Science and Business Media LLC
dc.relation.ispartofjournalNeural Computing and Applications
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchElectrical and Electronic Engineering
dc.subject.fieldofresearchCognitive Sciences
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode0906
dc.subject.fieldofresearchcode1702
dc.titlePrivacy protection of online social network users, against attribute inference attacks, through the use of a set of exhaustive rules
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationReza, KJ; Islam, MZ; Estivill-Castro, V, Privacy protection of online social network users, against attribute inference attacks, through the use of a set of exhaustive rules, Neural Computing and Applications, 2021
dc.date.updated2021-04-23T00:24:42Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2021 The Natural Computing Applications Forum. Published by Springer London. This is an electronic version of an article published in Neural Computing and Applications, 2021. Neural Computing and Applications is available online at: http://link.springer.com/ with the open URL of your article.
gro.hasfulltextFull Text
gro.griffith.authorEstivill-Castro, Vladimir


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