Fuzz-classification(p,l)-Angel:An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches  

作　　者：Tehsin Kanwal Hasina Attaullah Adeel Anjum Abid Khan Gwanggil Jeon 

机构地区：[1]Department of Computer Science,COMSATS University Islamabad,Pakistan [2]Department of Information Technology,Quaid-e-Azam University Islamabad,Pakistan [3]College of Science and Engineering,School of Computing and Maths,University of Derby,DE221GB,UK [4]Department of Embedded Systems Engineering,Incheon National University,South Korea

出　　处：《Digital Communications and Networks》2023年第5期1131-1140,共10页数字通信与网络（英文版）

摘　　要：The inability of traditional privacy-preserving models to protect multiple datasets based on sensitive attributes has prompted researchers to propose models such as SLOMS,SLAMSA,(p,k)-Angelization,and(p,l)-Angelization,but these were found to be insufficient in terms of robust privacy and performance.(p,l)-Angelization was successful against different privacy disclosures,but it was not efficient.To the best of our knowledge,no robust privacy model based on fuzzy logic has been proposed to protect the privacy of sensitive attributes with multiple records.In this paper,we suggest an improved version of(p,l)-Angelization based on a hybrid AI approach and privacy-preserving approach like Generalization.Fuzz-classification(p,l)-Angel uses artificial intelligence based fuzzy logic for classification,a high-dimensional segmentation technique for segmenting quasi-identifiers and multiple sensitive attributes.We demonstrate the feasibility of the proposed solution by modelling and analyzing privacy violations using High-Level Petri Nets.The results of the experiment demonstrate that the proposed approach produces better results in terms of efficiency and utility.

关 键 词：Generalization FUZZY-LOGIC MSA Privacy disclosures Membership function (p l)-Angelization QT HLPN 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TN91[自动化与计算机技术—控制科学与工程]