Abstract
With the advent of latest data mining techniques, preserving the privacy of individual’s data became a persistent issue. Every day tremendous amount of data is being generated electronically with increasing concern of data privacy. Such data when gets disseminated among various data analysts, the privacy of individuals may be breached, as the released information may be personal and sensitive in nature. Irrespective of the type of data whether numerical, categorical, mixed, time series etc, accurate analyses of such data with privacy preservation is a pervasive task. And due to the complex nature of time series data, analyzing such kind of data without harming its privacy is an open and challenging issue. In this paper we have addressed the issue of analyzing records with preserved privacy, and the data under consideration are expressed in terms of numerical time series of equal length. We have developed a data perturbation method with wavelet representation of time series data. Our experimental results show that the proposed method is effective in preserving the trade-off between data utility and privacy of time series.
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Chettri, S.K., Borah, B. (2014). Privacy Preservation of Time Series Data Using Discrete Wavelet Transforms. In: Kumar Kundu, M., Mohapatra, D., Konar, A., Chakraborty, A. (eds) Advanced Computing, Networking and Informatics- Volume 2. Smart Innovation, Systems and Technologies, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-07350-7_28
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DOI: https://doi.org/10.1007/978-3-319-07350-7_28
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07349-1
Online ISBN: 978-3-319-07350-7
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