DocumentCode
2258443
Title
Privacy-Preserving Classification on Horizontally Partitioned Data
Author
Tian, Tian ; Hua, Duan ; Guoping, He
Author_Institution
Coll. of Inf. Sci. & Eng., Shandong Univ. of Sci. & Technol., Qingdao, China
fYear
2010
fDate
11-14 Dec. 2010
Firstpage
230
Lastpage
233
Abstract
With the appearance of large-scale database and people´s increasing concern about individual privacy, privacy-preserving data mining becomes a hot study area, to which the support vector machine(SVM) belongs. In this paper, a novel privacy-preserving SVM for horizontally partitioned data is given. It has comparable accuracy to that of an ordinary SVM as we obtain the SVM by using the distinct property of the orthogonal matrices.
Keywords
data mining; data privacy; database management systems; matrix algebra; pattern classification; support vector machines; horizontally partitioned data; large-scale database; orthogonal matrix; privacy-preserving classification; privacy-preserving data mining; support vector machine; data mining; horizontally partitioned data; orthogonal matrix; privacy-preserving; support vector machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Security (CIS), 2010 International Conference on
Conference_Location
Nanning
Print_ISBN
978-1-4244-9114-8
Electronic_ISBN
978-0-7695-4297-3
Type
conf
DOI
10.1109/CIS.2010.56
Filename
5696269
Link To Document