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International Conference on Intelligent Computing

ICIC 2018: Intelligent Computing Theories and Application pp 454–466Cite as

Frequent Sequence Pattern Mining with Differential Privacy

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10954))

Abstract

Focusing on the issue that releasing frequent sequence patterns and the corresponding true supports may reveal the individuals’ privacy when the data set contains sensitive information, a Differential Private Frequent Sequence Mining (DPFSM) algorithm was proposed. Downward closure property was used to generate a candidate set of sequence patterns, smart truncating based technique was used to sample frequent patterns in the candidate set, and geometric mechanism was utilized to perturb the true supports of each sampled pattern. In addition, to improve the usability of the results, a threshold modification method was proposed to reduce truncation error and propagation error in mining process. The theoretical analysis show that the proposed method is ε-differentially private. The experimental results demonstrate that the proposed method has lower False Negative Rate(FNR) and Relative Support Error (RSE) than that of the comparison algorithm named PFS2, thus effectively improving the accuracy of mining results.

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Acknowledgment

This work was supported in part by Research Project of Hubei Provincial Department of Education (No. B2017590).

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Authors and Affiliations

  1. Faculty of Information Technology, Wuhan College of Foreign Language and Foreign Affairs, Wuhan, 430083, China

    Fengli Zhou & Xiaoli Lin

Authors
  1. Fengli Zhou
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  2. Xiaoli Lin
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Corresponding author

Correspondence to Fengli Zhou .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytechnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

  4. Indian Institute of Technology Kanpur, Kanpur, India

    Phalguni Gupta

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Zhou, F., Lin, X. (2018). Frequent Sequence Pattern Mining with Differential Privacy. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_42

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  • DOI: https://doi.org/10.1007/978-3-319-95930-6_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95929-0

  • Online ISBN: 978-3-319-95930-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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