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.
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Costa da Silva, J., Klusch, M. (2007). Privacy-Preserving Discovery of Frequent Patterns in Time Series. In: Perner, P. (eds) Advances in Data Mining. Theoretical Aspects and Applications. ICDM 2007. Lecture Notes in Computer Science(), vol 4597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73435-2_25
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DOI: https://doi.org/10.1007/978-3-540-73435-2_25
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