PDF(Pubmed)
Abstract：
The topic of privacy-preserving collaborative filtering is gaining more and more attention. Nevertheless, privacy-preserving collaborative filtering techniques are vulnerable to shilling or profile injection assaults. Hence, it is crucial to identify counterfeit profiles in order to achieve total success. Various techniques have been devised to identify and prevent intrusion patterns from infiltrating the system. Nevertheless, these strategies are specifically designed for collaborative filtering algorithms that do not prioritize privacy. There is a scarcity of research on identifying shilling attacks in recommender systems that prioritize privacy. This work presents a novel technique for identifying shilling assaults in privacy-preserving collaborative filtering systems. We employ an ant colony clustering detection method to effectively identify and eliminate fake profiles that are created by six widely recognized shilling attacks on compromised data. The objective of the study is to categorize the fraudulent profiles into a specific cluster and separate this cluster from the system. Empirical experiments are conducted with actual data. The empirical findings demonstrate that the strategy derived from the study effectively eliminates fraudulent profiles in privacy-preserving collaborative filtering.
摘要：
隐私保护协同过滤的话题越来越受到关注。然而，隐私保护协同过滤技术容易受到先令或配置文件注入攻击。因此，为了获得完全成功，识别假冒配置文件至关重要。已经设计了各种技术来识别和防止侵入模式渗入系统。然而，这些策略是专门为不优先考虑隐私的协同过滤算法设计的。在优先考虑隐私的推荐系统中识别先令攻击的研究很少。这项工作提出了一种新颖的技术，用于识别隐私保护协作过滤系统中的先令攻击。我们采用蚁群聚类检测方法来有效地识别和消除由六种广泛认可的先令攻击对受损数据创建的虚假配置文件。该研究的目的是将欺诈性档案分类为特定的集群，并将该集群与系统分开。用实际数据进行了实证实验。实证结果表明，该研究得出的策略有效地消除了隐私保护协同过滤中的欺诈性个人资料。
