Privacy-Preserving Collaborative Anomaly Detection for Participatory Sensing

  • Sarah M. Erfani
  • Yee Wei Law
  • Shanika Karunasekera
  • Christopher A. Leckie
  • Marimuthu Palaniswami
Conference paper

DOI: 10.1007/978-3-319-06608-0_48

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8443)
Cite this paper as:
Erfani S.M., Law Y.W., Karunasekera S., Leckie C.A., Palaniswami M. (2014) Privacy-Preserving Collaborative Anomaly Detection for Participatory Sensing. In: Tseng V.S., Ho T.B., Zhou ZH., Chen A.L.P., Kao HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science, vol 8443. Springer, Cham

Abstract

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.

Keywords

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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