High Utility-Itemset Mining and Privacy-Preserving Utility Mining

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

High Utility-Itemset Mining and Privacy-Preserving Utility Mining

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

High Utility-Itemset Mining and Privacy-Preserving Utility Mining

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Perspectives in Science

ISSN

2213-0209

e-ISSN

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Svazek periodika

7

Číslo periodika v rámci svazku

March

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

7

Strana od-do

74-80

Kód UT WoS článku

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EID výsledku v databázi Scopus

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