Frequent itemset hiding revisited: pushing hiding constraints into mining

作     者：Verykios, Vassilios S. Stavropoulos, Elias C. Krasadakis, Panteleimon Sakkopoulos, Evangelos 

作者机构：Hellen Open Univ Sch Sci & Technol Patras Greece Univ Piraeus Dept Informat Piraeus Greece 

出 版 物：《APPLIED INTELLIGENCE》 (应用智能)

年 卷 期：2022年第52卷第3期

页      面：2539-2555页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：department of Informatics in the University of Piraeus 

主　　题：Privacy preserving data mining Knowledge hiding Frequent itemset hiding Constraint-based data mining Linear programming 

摘      要：This paper introduces a new theoretical scheme for the solution of the frequent itemset hiding problem. We propose an algorithmic approach that consists of a novel constraint-based hiding model which encompasses hiding into one pass mining, along with a solution methodology that relies on Linear Programming. The induced patterns by the constraint-based mining algorithm are, in this way, utilized to build a minimal linear program whose solution dictates the construction of a database extension that delivers the sought-for hiding. This extension should be appended to the original database and released as a whole for mining, with that resulting extended database hiding the sensitive knowledge that we want to protect. Our proposed theory outdoes both in space complexity and accuracy, all the existing approaches which have been proposed so far in this domain and we proved that superiority with a series of experiments against other existing approaches. Our proposal sheds a new light on the exploration of new algorithmic techniques which can be handily applied to model hiding problems by providing solutions that computationally outperform all existing modeling approaches for hiding.