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 Na\(\ddot{\text {i}}\)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.

恶意数据挖掘者可以通过对用户的公开信息进行数据挖掘来推断在线社交网络（OSN）中用户的私人信息。通过探索有关目标的公共信息 如果用户（即隐私受到攻击的一个或一组OSN用户），攻击者可以准备一个训练数据集，其中包含与其他公开披露其数据的用户有关的类似信息。攻击者可以使用机器学习分类器，将未受攻击的用户的已发布信息作为非类属性输入，并提取私有信息作为类属性。某些技术针对特定分类器提供了一些隐私保护；但是，如果攻击者使用其他分类器（而不是隐私保护技术所使用的分类器）来推断敏感信息，则所提供的隐私可能会受到威胁。实际上，很难预测涉及隐私攻击的分类器。在这项研究中，我们提出了一种隐私保护技术，该技术首先以攻击者可以准备的类似方式准备训练数据集，然后采用独立于分类器的方法从训练数据集中提取模式（或逻辑规则）。然后，基于提取的规则集，建议目标用户隐藏一些非类属性值和/或修改一些友谊链接以保护其隐私。我们将建议的技术应用于包含用户属性值及其友情链接的两个OSN数据集。为了评估所提出技术的性能，我们使用了传统的分类器，例如Na 然后建议目标用户隐藏一些非类属性值和/或修改一些友谊链接以保护其隐私。我们将建议的技术应用于包含用户属性值及其友情链接的两个OSN数据集。为了评估所提出技术的性能，我们使用了传统的分类器，例如Na 然后建议目标用户隐藏一些非类属性值和/或修改一些友谊链接以保护其隐私。我们将建议的技术应用于包含用户属性值及其友情链接的两个OSN数据集。为了评估所提出技术的性能，我们使用了传统的分类器，例如Na\（\ ddot {\ text {i}} \） ve贝叶斯，支持向量机和随机森林，位于受隐私保护的数据集上。实验结果表明，我们提出的技术在保持数据实用性的同时保护隐私方面优于现有的隐私保护算法。