Privacy protection of online social network users, against attribute inference attacks, through the use of a set of exhaustive rules

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• dc.contributor.author Reza, Khondker Jahir
• dc.contributor.author Idlam, Zahidul
• dc.contributor.author Estivill-Castro, V. (Vladimir)
• dc.date.accessioned 2021-05-26T07:59:26Z
• dc.date.issued 2021
• dc.description.abstract A 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.format.mimetype application/pdf
• dc.identifier.citation Reza KJ, Islam Z, Estivill-Castro V. Privacy protection of online social network users, against attribute inference attacks, through the use of a set of exhaustive rules. Neural Comput & Applic. 2021;33(19):12397-427. DOI: 10.1007/s00521-021-05860-8
• dc.identifier.doi http://dx.doi.org/10.1007/s00521-021-05860-8
• dc.identifier.issn 0941-0643
• dc.identifier.uri http://hdl.handle.net/10230/47659
• dc.language.iso eng
• dc.publisher Springer
• dc.relation.ispartof Neural Comput & Applic. 2021;33(19):12397-427.
• dc.rights © Springer The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-021-05860-8
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Attribute inference attack
• dc.subject.keyword Data mining
• dc.subject.keyword Online social networks
• dc.subject.keyword Privacy preserving technique
• dc.title Privacy protection of online social network users, against attribute inference attacks, through the use of a set of exhaustive rules
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion