![]() Yin, Research of detection algorithm for time series abnormal subsequence, International Conference of Pioneering Computer Scientists, Engineers and Educators, Springer, 2017, 12-26. Tang, On complementarity of cluster and outlier detection schemes, International Conference on Data Warehousing and Knowledge Discovery, Springer, 2003,234-243. Deng, Discovering cluster-based local outliers, Pattern Recognit. Borgwardt, Rapid distance-based outlier detection via sampling, Advances in Neural Information Processing Systems, 2013,467-475. Available from: A Fast DistanceBased Algorithm to Detect Outliers. Wilkes, A fast distance-based outlier detection technique, Industrial Conference on Data Mining-Poster and Workshop, 2008, 25-44. Ng, Algorithms for mining distancebased outliers in large datasets, Proceedings of the international conference on very large data bases, Citeseer, 1998,392-403. Hawkins, Identification of Outliers, Springer, (1980).Į. Privacy preserving anomaly detection based on local density estimation. 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.Ĭitation: Chunkai Zhang, Ao Yin, Wei Zuo, Yingyang Chen. From security analysis, our scheme will not leak privacy information of participants. Compared with existing schemes with privacy preserving, our scheme needs less communication cost and less calculation cost. 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. The local density of each feature can be estimated by the defined mapping function. 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. In this paper, we propose a fast anomaly detection algorithm based on local density estimation (LDEM). Another important problem is how to efficiently detect anomalies while protecting data privacy. However, most existing algorithms have high time complexity. Anomaly detection has been widely researched in financial, biomedical and other areas.
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