PPLDEM: A Fast Anomaly Detection Algorithm with Privacy Preserving

  • Ao Yin
  • Chunkai ZhangEmail author
  • Zoe L. JiangEmail author
  • Yulin Wu
  • Xing Zhang
  • Keli Zhang
  • Xuan Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11337)


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.


Anomaly detection Local density Privacy preserving 



This study was supported by the Shenzhen Research Council (Grant No. JSGG20170822160842949, JCYJ20170307151518535).


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyHarbin Institute of TechnologyShenzhenChina
  2. 2.National Engineering Laboratory for Big Data Collaborative Security TechnologyBeijingChina

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