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.
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Lin, CW., Hong, TP., Hsu, HC. (2014). Hiding Sensitive Itemsets with Minimal Side Effects in Privacy Preserving Data Mining. In: Pan, JS., Snasel, V., Corchado, E., Abraham, A., Wang, SL. (eds) Intelligent Data analysis and its Applications, Volume I. Advances in Intelligent Systems and Computing, vol 297. Springer, Cham. https://doi.org/10.1007/978-3-319-07776-5_10
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DOI: https://doi.org/10.1007/978-3-319-07776-5_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07775-8
Online ISBN: 978-3-319-07776-5
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