Enhanced Clustering Based OSN Privacy Preservation to Ensure k-Anonymity, t-Closeness, l-Diversity, and Balanced Privacy Utility  

作　　者：Rupali Gangarde Amit Sharma Ambika Pawar 

机构地区：[1]Department of CSE,Lovely Professional University,Phagwara,144411,India [2]Department of CSE,Symbiosis Institute of Technology(SIT),Affiliated to Symbiosis International(Deemed University),Pune,412115,India [3]School of Computer Applications,Lovely Professional University,Phagwara,144411,India [4]Learning&Development,Persistent University,Persistent Systems,Pune,411057,India

出　　处：《Computers, Materials & Continua》2023年第4期2171-2190,共20页计算机、材料和连续体（英文）

摘　　要：Online Social Networks (OSN) sites allow end-users to share agreat deal of information, which may also contain sensitive information,that may be subject to commercial or non-commercial privacy attacks. Asa result, guaranteeing various levels of privacy is critical while publishingdata by OSNs. The clustering-based solutions proved an effective mechanismto achieve the privacy notions in OSNs. But fixed clustering limits theperformance and scalability. Data utility degrades with increased privacy,so balancing the privacy utility trade-off is an open research issue. Theresearch has proposed a novel privacy preservation model using the enhancedclustering mechanism to overcome this issue. The proposed model includesphases like pre-processing, enhanced clustering, and ensuring privacy preservation.The enhanced clustering algorithm is the second phase where authorsmodified the existing fixed k-means clustering using the threshold approach.The threshold value is determined based on the supplied OSN data of edges,nodes, and user attributes. Clusters are k-anonymized with multiple graphproperties by a novel one-pass algorithm. After achieving the k-anonymityof clusters, optimization was performed to achieve all privacy models, suchas k-anonymity, t-closeness, and l-diversity. The proposed privacy frameworkachieves privacy of all three network components, i.e., link, node, and userattributes, with improved utility. The authors compare the proposed techniqueto underlying methods using OSN Yelp and Facebook datasets. The proposedapproach outperformed the underlying state of art methods for Degree ofAnonymization, computational efficiency, and information loss.

关 键 词：Enhanced clustering online social network K-ANONYMITY t-closeness l-diversity privacy preservation 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]