Efficient distributed privacy-preserving collaborative outlier detection

Abstract

As a common way to identify anomalous data, outlier detection is widely applicable for intrusions detection, adverse reactions analysis, financial fraud prevention, etc. The accuracy of outlier detection depends crucially on the number of data involved in the test, i.e., the more data participate in detection, the higher accuracy we get. For this reason, cross-dataset collaborative outlier detection is introduced to conquer the lack of data in a single-dataset setting. However, privacy concerns seriously prevent the application of collaborative outlier detection, since most organization are unwilling to share their data with others directly in practice. In this paper, we present efficient protocols for privacy preserving collaborative outlier detection from arbitrarily partitioned data using Local Distance-based Outlier Factor (LDOF). Our protocols fall in the two-server model where data owners distribute their private data among two non-colluding servers who detect outlier on the joint data by secure two-party computation. In particular, we perform arithmetic operations which takes place inside LDOF on arithmetic circuits instead of boolean circuits, and perform sorting operations on boolean circuits. Such a design enables standard operations are performed with suitable circuits, and thus our scheme is more efficient. In addition, to further improve protocol efficiency, local sensitive hash (LSH) is utilized to filter out data which do not need secure computation to reduce the the amount of shared data. We implement our system in C++ on real data. The security analysis and experiments show the security and efficiency of the proposed scheme. Our protocols are more faster than the state of previous methods.