Outsourced privacy-preserving anomaly detection in time series of multi-party
Chunkai Zhang, Wei Zuo, Peng Yang, Ye Li, Xuan Wang · China Communications · 2022
Anomaly detection has practical significance for finding unusual patterns in time series. However, most existing algorithms may lose some important information in time series presentation and have high time complexity. Another problem is that privacy-preserving was not taken into account in these algorithms. In this paper, we propose a new data structure named Interval Hash Table (IHTable) to capture more original information of time series and design a fast anomaly detection algorithm based on Interval Hash Table (ADIHT). The key insight of ADIHT is distributions of normal subsequences are always similar while distributions of anomaly subsequences are different and random by contrast. Furthermore, to make our proposed algorithm fit for anomaly detection under multiple participation, we propose a privacy-preserving anomaly detection scheme named OP-ADIHT based on ADIHT and homomorphic encryption. Compared with existing anomaly detection schemes with privacy-preserving, OP-ADIHT needs less communication cost and calculation cost. Security analysis of different circumstances also shows that OP-ADIHT will not leak the privacy information of participants. Extensive experiments results show that ADIHT can outperform most anomaly detection algorithms and perform close to the best results in terms of AUC-ROC, and ADIHT needs the least time.