Privacy-preserving discovery of frequent patterns in time series

  • Authors:
  • Josenildo Costa da Silva;Matthias Klusch

  • Affiliations:
  • German Research Center for Artificial Intelligence Deduction and Multiagent Systems, Saarbruecken, Germany;German Research Center for Artificial Intelligence Deduction and Multiagent Systems, Saarbruecken, Germany

  • Venue:
  • ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
  • Year:
  • 2007

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Abstract

We present DPD-HE, a privacy preserving algorithm for mining time series data. We assume data is split among several sites. The problem is to find all frequent subsequences of time series without revealing local data to any site. Our solution exploit density estimate and secure multiparty computation techniques to provide privacy to a given extent.