DocumentCode
2850240
Title
Privacy-preserving outlier detection
Author
Vaidya, Jaideep ; Clifton, Chris
Author_Institution
Rutgers Univ., Newark, NJ, USA
fYear
2004
fDate
1-4 Nov. 2004
Firstpage
233
Lastpage
240
Abstract
Outlier detection can lead to the discovery of truly unexpected knowledge in many areas such as electronic commerce, credit card fraud and especially national security. We look at the problem of finding outliers in large distributed databases where privacy/security concerns restrict the sharing of data. Both homogeneous and heterogeneous distribution of data is considered. We propose techniques to detect outliers in such scenarios while giving formal guarantees on the amount of information disclosed.
Keywords
data mining; data privacy; distributed databases; security of data; very large databases; data sharing; heterogeneous data distribution; homogeneous data distribution; knowledge discovery; large distributed databases; privacy-preserving outlier detection; Credit cards; Data mining; Data privacy; Data security; Distributed databases; Electronic commerce; Information security; National security; Protocols; Terrorism;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Mining, 2004. ICDM '04. Fourth IEEE International Conference on
Print_ISBN
0-7695-2142-8
Type
conf
DOI
10.1109/ICDM.2004.10081
Filename
1410289
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