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Privacy-Preserving Discovery of Frequent Patterns in Time Series

Josenildo Costa da Silva; Matthias Klusch
In: Petra Perner (Hrsg.). Advances in Data Mining. Theoretical Aspects and Applications. Industrial Conference on Data Mining (ICDM-2007), 7th, July 14-18, Leipzig, Germany, Pages 318-328, Lecture Notes in Computer Science (LNCS), Vol. 4597, Springer-Verlag, 2007.


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.