High Utility-Itemset Mining and Privacy-Preserving Utility Mining

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86094385" target="_blank" >RIV/61989100:27240/16:86094385 - isvavai.cz</a>
Result on the web
<a href="http://www.sciencedirect.com/science/article/pii/S2213020915000580" target="_blank" >http://www.sciencedirect.com/science/article/pii/S2213020915000580</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.pisc.2015.11.013" target="_blank" >10.1016/j.pisc.2015.11.013</a>

Result language
angličtina
Original language name
High Utility-Itemset Mining and Privacy-Preserving Utility Mining
Original language description
In recent decades, high-utility itemset mining (HUIM) has emerging a critical research topic since the quantity and profit factors are both concerned to mine the high-utility itemsets (HUIs). Generally, data mining is commonly used to discover interesting and useful knowledge from massive data. It may, however, lead to privacy threats if private or secure information (e.g., HUIs) are published in the public place or misused. In this paper, we focus on the issues of HUIM and privacy-preserving utility mining (PPUM), and present two evolutionary algorithms to respectively mine HUIs and hide the sensitive high-utility itemsets in PPUM. Extensive experiments showed that the two proposed models for the applications of HUIM and PPUM can not only generate the high quality profitable itemsets according to the user-specified minimum utility threshold, but also enable the capability of privacy preserving for private or secure information (e.g., HUIs) in real-word applications.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JC - Computer hardware and software
OECD FORD branch
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Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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