Privacy preserving anomaly detection based on local density estimation

Abstract

Anomaly detection has been widely researched in financial, biomedical and other areas. However, most existing algorithms have high time complexity. Another important problem is how to efficiently detect anomalies while protecting data privacy. In this paper, we propose a fast anomaly detection algorithm based on local density estimation (LDEM). The key insight of LDEM is a fast local density estimator, which estimates the local density of instances by the average density of all features. The local density of each feature can be estimated by the defined mapping function. Furthermore, we propose an efficient scheme named PPLDEM based on the proposed scheme and homomorphic encryption to detect anomaly instances in the case of multi-party participation. Compared with existing schemes with privacy preserving, our scheme needs less communication cost and less calculation cost. From security analysis, our scheme will not leak privacy information of participants. And experiments results show that our proposed scheme PPLDEM can detect anomaly instances effectively and efficiently, for example, the recognition of activities in clinical environments for healthy older people aged 66 to 86 years old using the wearable sensors.