DocumentCode :
2030342
Title :
Full-scale privacy preserving for association rule mining
Author :
Ma, Tinghuai ; Leng, Jiazhao ; Li, Keyi
Author_Institution :
Sch. of Comput. & Software, Nanjing Univ. of Inf. Sci. & Technol., Nanjing, China
Volume :
4
fYear :
2010
fDate :
10-12 Aug. 2010
Firstpage :
1668
Lastpage :
1672
Abstract :
Privacy has become an important issue in Data Mining. Many methods have been brought out to solve this problem. This paper deals with the problem of association rule mining which preserve the confidentiality of each database. In order to find the association rule, each participant has to share their own data. Thus, much privacy information may be broadcasted or been illegal used. These issues can be divided into three categories: data hiding, knowledge hiding and data mining results publishing. This paper reviews the major method of privacy preserving on each category and choose some of them to complete our system. At the end, an improvement of sensitive rules hiding is proposed to make it more accuracy and security.
Keywords :
data encapsulation; data mining; data privacy; association rule mining; data hiding; data mining results publishing; database confidentiality; full-scale privacy preserving; knowledge hiding; Association rules; Itemsets; Privacy; Publishing; Security; association rule; data hinding; knowledge hiding; privacy preserving; results publishing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-5931-5
Type :
conf
DOI :
10.1109/FSKD.2010.5569380
Filename :
5569380
Link To Document :
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