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On analysis of time-series data with preserved privacy

  • S.I. : ICACNI 2014
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Abstract

Time-series data analysis with privacy preservation is an open and challenging issue. To name a few are like analyzing company’s confidential financial data, individual’s health-related data, electricity consumption of individual’s households and so on. Due to the complex nature of time-series data, analyzing such data without any revelation of sensitive information to adversaries is a pervasive task. Here, we have addressed the issue of analyzing numerical time-series of equal length with preserved privacy. Considering the Discrete Wavelet Transform as a suitable technique for transforming time-series in frequency–time representation, we have applied the concept in privacy-preserving analysis of such data. Experimental results show that our proposed method is superior to the existing methods in preserving the trade-off between data utility and privacy. The privacy models developed using the proposed method are also evaluated in terms of clustering and classification accuracies obtained from perturbed time-series data.

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Authors and Affiliations

  1. Department of Computer Science Engineering and Information Technology, Assam Don Bosco University, Guwahati, India

    Sarat Kumar Chettri

  2. Department of Computer Science and Engineering, Tezpur University, Tezpur, India

    Bhogeswar Borah

Authors
  1. Sarat Kumar Chettri
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  2. Bhogeswar Borah
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Correspondence to Sarat Kumar Chettri.

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Chettri, S.K., Borah, B. On analysis of time-series data with preserved privacy. Innovations Syst Softw Eng 11, 155–165 (2015). https://doi.org/10.1007/s11334-015-0249-3

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  • DOI: https://doi.org/10.1007/s11334-015-0249-3

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