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dc.contributor.authorBostanoglu, Belgin Ergenc
dc.contributor.authorOzturk, Ahmet Cumhur
dc.date.accessioned2020-07-18T08:34:02Z
dc.date.available2020-07-18T08:34:02Z
dc.date.issued2020
dc.identifier.issn1932-1864
dc.identifier.issn1932-1872
dc.identifier.urihttps://doi.org/10.1002/sam.11458
dc.identifier.urihttps://hdl.handle.net/11147/8831
dc.descriptionWOS: 000527077200001en_US
dc.description.abstractPrivacy preserving data mining (PPDM) is the process of protecting sensitive knowledge from being discovered by data mining techniques in case of data sharing. Privacy preserving frequent itemset mining (PPFIM) is a subtask and NP-hard problem of PPDM. Its objective is to modify a given database in such a way that none of the sensitive itemsets of the database owner can be obtained by any frequent itemset mining technique from the modified database. The main challenge of PPFIM is to minimize the distortion given to the data and nonsensitive knowledge while sanitizing all given sensitive itemsets. Distortion-based sensitive itemset hiding algorithms decrease the support of each sensitive itemset under a predefined sensitive threshold through sanitization. Most of the distortion-based itemset hiding algorithms allow database owner to define a single sensitive threshold for each sensitive itemset. However, this is a limitation to the database owner since the importance of each sensitive itemset varies. In this paper we propose a distortion-based itemset hiding algorithm that allows database owner to assign multiple sensitive thresholds, namely itemset oriented pseudo graph based sanitization (IPGBS) algorithm. The purpose of IPGBS algorithm is to give minimum distortion to the nonsensitive knowledge and data while hiding all sensitive itemsets. For this reason, the IPGBS algorithm modifies least amount of transaction and transaction content. The performance evaluation of the IPGBS algorithm is conducted by using two different counterparts on four different databases. The results show that the IPGBS algorithm is more efficient in terms of nonsensitive frequent itemset loss on both dense and sparse databases. It has considerable good results in terms of number of transactions modified, number of items deleted, execution time and total memory allocation as well.en_US
dc.language.isoengen_US
dc.publisherWileyen_US
dc.relation.isversionof10.1002/sam.11458en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectinformation lossen_US
dc.subjectitemset miningen_US
dc.subjectprivacy preserving itemset miningen_US
dc.titleMinimizing information loss in shared data: Hiding frequent patterns with multiple sensitive support thresholdsen_US
dc.typearticleen_US
dc.relation.journalStatistical Analysis And Data Miningen_US
dc.contributor.departmentIzmir Institute of Technologyen_US
dc.identifier.volume13en_US
dc.identifier.issue4en_US
dc.identifier.startpage309en_US
dc.identifier.endpage323en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.cont.department-temp[Bostanoglu, Belgin Ergenc] Izmir Inst Technol, Dept Comp Engn, Izmir, Turkey; [Ozturk, Ahmet Cumhur] Adnan Menderes Univ, Dept Comp Aided Design & Animat, Aydin, Turkeyen_US


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