Name
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/CIS.2010.56 | CIS |
Keywords | Field | DocType |
database management systems,partitioned data,distinct property,data privacy,pattern classification,hot study area,matrix algebra,privacy-preserving classification,privacy-preserving data mining,comparable accuracy,support vector machine,data mining,orthogonal matrix,privacy-preserving,large-scale database,individual privacy,support vector machines,ordinary svm,horizontally partitioned data,accuracy,data models,kernel,computational modeling | Kernel (linear algebra),Data mining,Data modeling,Orthogonal matrix,Ranking SVM,Computer science,Matrix algebra,Support vector machine,Information privacy | Conference |
ISBN | Citations | PageRank |
978-0-7695-4297-3 | 0 | 0.34 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tian Tian | 1 | 8 | 7.32 |
| Duan Hua | 2 | 0 | 0.34 |
| Guoping He | 3 | 91 | 13.59 |