Privacy-Preserving Collaborative Anomaly Detection for Participatory Sensing

  • Sarah M. Erfani
  • Yee Wei Law
  • Shanika Karunasekera
  • Christopher A. Leckie
  • Marimuthu Palaniswami
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8443)


In collaborative anomaly detection, multiple data sources submit their data to an on-line service, in order to detect anomalies with respect to the wider population. A major challenge is how to achieve reasonable detection accuracy without disclosing the actual values of the participants’ data. We propose a lightweight and scalable privacy-preserving collaborative anomaly detection scheme called Random Multiparty Perturbation (RMP), which uses a combination of nonlinear and participant-specific linear perturbation. Each participant uses an individually perturbed uniformly distributed random matrix, in contrast to existing approaches that use a common random matrix. A privacy analysis is given for Bayesian Estimation and Independent Component Analysis attacks. Experimental results on real and synthetic datasets using an auto-encoder show that RMP yields comparable results to non-privacy preserving anomaly detection.


Privacy-preserving data mining Anomaly detection Collaborative learning Participatory sensing Horizontally partitioned data 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sarah M. Erfani
    • 1
  • Yee Wei Law
    • 2
  • Shanika Karunasekera
    • 1
  • Christopher A. Leckie
    • 1
  • Marimuthu Palaniswami
    • 2
  1. 1.Department of Computing and Information SystemsNICTA Victoria Research LaboratoryAustralia
  2. 2.Department of Electrical and Electronic EngineeringThe University of MelbourneAustralia

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