Fuzz-classification(p,l)-Angel:An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches
作者机构：Department of Computer ScienceCOMSATS University IslamabadPakistan Department of Information TechnologyQuaid-e-Azam University IslamabadPakistan College of Science and EngineeringSchool of Computing and MathsUniversity of DerbyDE221GBUK Department of Embedded Systems EngineeringIncheon National UniversitySouth Korea
出 版 物：《Digital Communications and Networks》 (数字通信与网络（英文版）)
年 卷 期：2023年第9卷第5期
页 面：1131-1140页
核心收录：
学科分类：0810[工学-信息与通信工程] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 081001[工学-通信与信息系统] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Generalization Fuzzy-logic MSA Privacy disclosures Membership function (p,l)-Angelization QT HLPN
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 *** 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 *** this paper,we suggest an improved version of(p,l)-Angelization based on a hybrid AI approach and privacy-preserving approach like ***-classification(p,l)-Angel uses artificial intelligence based fuzzy logic for classification,a high-dimensional segmentation technique for segmenting quasi-identifiers and multiple sensitive *** demonstrate the feasibility of the proposed solution by modelling and analyzing privacy violations using High-Level Petri *** results of the experiment demonstrate that the proposed approach produces better results in terms of efficiency and utility.
维普期刊数据库