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dc.contributor.authorGunawan, Dedi
dc.contributor.authorWantoro, Jan
dc.date.accessioned2016-02-05T06:29:30Z
dc.date.available2016-02-05T06:29:30Z
dc.date.issued2015-10-15
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dc.identifier.issn2470-4330
dc.identifier.urihttp://hdl.handle.net/11617/6567
dc.description.abstractData mining algorithms give advantages on data analytics, thus the information which are hidden from database can be revealed maximally as a result the data owner may use it effectively. Besides the benefits, it also brings some challenges like some information which is considered as sensitive can be revealed under some algorithms. Sensitive information can be considered as the information of people or organization that should be kept under certain rule before it is published. Therefore, in this research we propose an efficient approach to deal with privacy preserving data mining (PPDM) for avoiding privacy breach in frequent itemsets mining. The size of database is also be considered, therefore we conduct data segregation in order to separate between transactions with sensitive itemsets and transactions without sensitive itemsets. This step is followed by deriving which item from transactions that is going to be replaced using unknown symbol to perform data sanitization. A set of experiment is conducted to show the benefit of our approach. Based on the experimental results, the proposed approach has good performance for hiding sensitive itemsets and also it results less changes in the original database.in_ID
dc.language.isoen_USin_ID
dc.publisherUniversitas Muhammadiyah Surakartain_ID
dc.subjectFrequent itemsetsin_ID
dc.subjectSensitive itemsets hidingin_ID
dc.subjectData Miningin_ID
dc.subjectUnknown Symbolin_ID
dc.titleProtecting Sensitive Frequent Itemsets in Database Transaction Using Unknown Symbolin_ID
dc.typeArticlein_ID


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