A New Privacy-Preserving Data Publishing Algorithm Utilizing Connectivity-Based Outlier Factor and Mondrian Techniques

作     者：Burak Cem Kara Can Eyüpoglu 

作者机构：Department of Computer EngineeringTurkish Air Force AcademyNational Defence UniversityYesilyurtIstanbulTurkey Department of Computer EngineeringAtatürk Strategic Studies and Graduate InstituteNational Defence UniversityBesiktasIstanbulTurkey 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第76卷第8期

页      面：1515-1535页

核心收录：

学科分类：0839[工学-网络空间安全] 08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Scientific and Technological Research Council of Turkiye under Project No.(122E670) 

主　　题：Data anonymization privacy-preserving data publishing k-anonymity generalization mondrian 

摘      要：Developing a privacy-preserving data publishing algorithm that stops individuals from disclosing their identities while not ignoring data utility remains an important goal to *** finding the trade-off between data privacy and data utility is an NP-hard problem and also a current research *** existing approaches are investigated,one of the most significant difficulties discovered is the presence of outlier data in the *** data has a negative impact on data ***,k-anonymity algorithms,which are commonly used in the literature,do not provide adequate protection against outlier *** this study,a new data anonymization algorithm is devised and tested for boosting data utility by incorporating an outlier data detection mechanism into the Mondrian *** connectivity-based outlier factor(COF)algorithm is used to detect *** is selected because of its capacity to anonymize multidimensional data while meeting the needs of real-world ***,on the other hand,is used to discover outliers in high-dimensional datasets with complicated *** proposed algorithm generates more equivalence classes than the Mondrian algorithm and provides greater data utility than previous algorithms based on *** addition,it outperforms other algorithms in the discernibility metric(DM),normalized average equivalence class size(Cavg),global certainty penalty(GCP),query error rate,classification accuracy(CA),and F-measure ***,the increase in the values of theGCPand error ratemetrics demonstrates that the proposed algorithm facilitates obtaining higher data utility by grouping closer data points when compared to other algorithms.