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- W57253943 abstract "Privacy-preserving data mining (PPDM) has become an important issue to hide the confidential or private data before it is shared or published in recent years. In this paper, a novel algorithm is proposed to hide sensitive itemsets through item deletion. Three side effects of hiding failures, missing itemsets, and artificial itemsets are considered to evaluate whether the transactions or the itemsets are required to be deleted for hiding sensitive itemsets. Experiments are then conducted to show the performance of the proposed algorithm in execution time, number of deleted transactions, and number of side effects.KeywordsPrivacy-preserving data miningside effectsinformation hidingdata sanitizationsensitive itemsets" @default.
- W57253943 created "2016-06-24" @default.
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- W57253943 date "2014-01-01" @default.
- W57253943 modified "2023-10-18" @default.
- W57253943 title "Hiding Sensitive Itemsets with Minimal Side Effects in Privacy Preserving Data Mining" @default.
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- W57253943 doi "https://doi.org/10.1007/978-3-319-07776-5_10" @default.
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