Privacy-Preserving Outlier Detection for Data Streams

  • Jonas BöhlerEmail author
  • Daniel Bernau
  • Florian Kerschbaum
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10359)


In cyber-physical systems sensors data should be anonymized at the source. Local data perturbation with differential privacy guarantees can be used, but the resulting utility is often (too) low. In this paper we contribute an algorithm that combines local, differentially private data perturbation of sensor streams with highly accurate outlier detection. We evaluate our algorithm on synthetic data. In our experiments we obtain an accuracy of 80% with a differential privacy value of \(\epsilon = 0.1\) for well separated outliers.



This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 700294 (C3ISP) and 653497 (PANORAMIX).


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Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  • Jonas Böhler
    • 1
    Email author
  • Daniel Bernau
    • 1
  • Florian Kerschbaum
    • 2
  1. 1.SAP ResearchKarlsruheGermany
  2. 2.University of WaterlooWaterlooCanada

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