Multi-source data outlier detection based on secure multi-party computation

Abstract

Outlier detection has been applied to many fields such as financial fraud, fault detection, and health diagnosis as an important technology to discover abnormal data. Data sharing is required to perform outlier detection on multi-source data. However, data sharing between multi-source generally discloses privacy embedded within the data such as sensitive patient information. With the increasing emphasis on personal privacy, it is necessary to study how to achieve outlier detection for multi-source data while preserving privacy. Secure Multi-Party Computation (SMPC) is a privacy-preserving technology to achieve secure calculation between multi-source in the absence of a trusted third party. But due to frequent data interaction, high complexity and low practicability comes with complex calculations. In this paper, we propose a secure multi-source data outlier detection scheme based on SMPC. Our scheme uses homomorphic encryption and perturbation to preserve the critical process of calculating the global distance matrix, which greatly reduces the complexity of the secure calculation process. Besides, we design an outlier determination strategy to reduce the steps of searching reverse neighbors and calculating the final local outlier factor. By comparison, our scheme outperforms the existing schemes in terms of accuracy ratio, running time and efficiency.