Abstract
In this paper, we first propose a fast anomaly detection algorithm 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 PPLDEM to detect anomaly instances with considering privacy protection in the case of multi-party participation, based on the proposed scheme and homomorphic encryption. Compare with existing schemes with privacy preserving, our scheme needs less communication cost and less calculation. From security analysis, it can prove that our scheme will not leak any privacy information of participants. And experiments results show that our proposed scheme PPLDEM can detect anomaly instances effectively and efficiently.
Keywords
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Acknowledgment
This study was supported by the Shenzhen Research Council (Grant No. JSGG20170822160842949, JCYJ20170307151518535).
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Yin, A. et al. (2018). PPLDEM: A Fast Anomaly Detection Algorithm with Privacy Preserving. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11337. Springer, Cham. https://doi.org/10.1007/978-3-030-05063-4_28
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DOI: https://doi.org/10.1007/978-3-030-05063-4_28
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