DocumentCode :
3301671
Title :
Privacy preserving high utility mining based on genetic algorithms
Author :
Chun-Wei Lin ; Tzung-Pei Hong ; Jia-Wei Wong ; Guo-Cheng Lan
Author_Institution :
Innovative Inf. Ind. Res. Center, Harbin Inst. of Technol., Shenzhen, China
fYear :
2013
fDate :
13-15 Dec. 2013
Firstpage :
191
Lastpage :
195
Abstract :
In this paper, a GA-based privacy-preserving utility mining method is proposed to delete appropriate transactions for hiding sensitive high utility itemsets from a database. The downward closure property and the pre-large concepts are adopted in the proposed algorithm to reduce the cost of rescanning databases. Experiments are also conducted to evaluate the performance of the proposed approach in execution time and the amount of side-effects.
Keywords :
data mining; data privacy; database management systems; genetic algorithms; GA-based privacy-preserving utility mining method; downward closure property; execution time; genetic algorithms; pre-large concepts; Biological cells; Data mining; Genetic algorithms; Itemsets; Sociology; Statistics; evolutionary computation; genetic algorithm; pre-large concept; privacy preserving; utility mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Granular Computing (GrC), 2013 IEEE International Conference on
Conference_Location :
Beijing
Type :
conf
DOI :
10.1109/GrC.2013.6740406
Filename :
6740406
Link To Document :
بازگشت