Title :
Privacy-preserving outlier detection
Author :
Vaidya, Jaideep ; Clifton, Chris
Author_Institution :
Rutgers Univ., Newark, NJ, USA
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;
Conference_Titel :
Data Mining, 2004. ICDM '04. Fourth IEEE International Conference on
Print_ISBN :
0-7695-2142-8
DOI :
10.1109/ICDM.2004.10081