Title: On privacy-preserving time series data classification

Authors: Ye Zhu, Yongjian Fu, Huirong Fu

Addresses: Electrical and Computer Engineering Department, Cleveland State University, 2121 Euclid Ave., Cleveland, OH 44115, USA. ' Electrical and Computer Engineering Department, Cleveland State University, 2121 Euclid Ave., Cleveland, OH 44115, USA. ' Department of Computer Science and Engineering, Oakland University, Rochester, MI 48309-4478, USA

Abstract: In this paper, we propose discretisation-based schemes to preserve privacy in time series data mining. Traditional research on preserving privacy in data mining focuses on time-invariant privacy issues. With the emergence of time series data mining, traditional snapshot-based privacy issues need to be extended to be multi-dimensional with the addition of time dimension. In this paper, we defined three threat models based on trust relationship between the data miner and data providers. We propose three different schemes for these three threat models. The proposed schemes are extensively evaluated against public-available time series datasets. Our experiments show that proposed schemes can preserve privacy with cost of reduction in mining accuracy. For most datasets, proposed schemes can achieve low privacy leakage with slight reduction in classification accuracy. We also studied effect of parameters of proposed schemes in this paper.

Keywords: privacy preservation; time series data mining; classification; threat models; trust relationships.

DOI: 10.1504/IJDMMM.2010.032145

International Journal of Data Mining, Modelling and Management, 2010 Vol.2 No.2, pp.117 - 136

Published online: 11 Mar 2010 *

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