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

作　　者：Burak Cem Kara Can Eyüpoglu 

机构地区：[1]Department of Computer Engineering,Turkish Air Force Academy,National Defence University,Yesilyurt,Istanbul,Turkey [2]Department of Computer Engineering,Atatürk Strategic Studies and Graduate Institute,National Defence University,Besiktas,Istanbul,Turkey

出　　处：《Computers, Materials & Continua》2023年第8期1515-1535,共21页计算机、材料和连续体（英文）

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

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 achieve.Because finding the trade-off between data privacy and data utility is an NP-hard problem and also a current research area.When existing approaches are investigated,one of the most significant difficulties discovered is the presence of outlier data in the datasets.Outlier data has a negative impact on data utility.Furthermore,k-anonymity algorithms,which are commonly used in the literature,do not provide adequate protection against outlier data.In 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 algorithm.The connectivity-based outlier factor(COF)algorithm is used to detect outliers.Mondrian is selected because of its capacity to anonymize multidimensional data while meeting the needs of real-world data.COF,on the other hand,is used to discover outliers in high-dimensional datasets with complicated structures.The proposed algorithm generates more equivalence classes than the Mondrian algorithm and provides greater data utility than previous algorithms based on k-anonymization.In 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 metrics.Moreover,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.

关 键 词：Data anonymization privacy-preserving data publishing K-ANONYMITY GENERALIZATION MONDRIAN 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]