Privacy Preserving Outlier Detection over Vertically Partitioned Data

Abstract

Outlier detection has numerous useful applications such as detecting criminal activities in electronic commerce, terrorism prediction and exceptional cases in many areas. Privacy and security concerns, however, arise while performing mining for outliers on distributed data. In this paper, we present two privacy preserving distance-based outlier detection algorithms over vertically partitioned data, not disclosing any private information to any participant. The first is between two parties and the second among multi-parties. The security and performances such as computation and communication complexities are analyzed for both of the two privacy preserving algorithms.