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Multi-question Negative Surveys

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Data Mining and Big Data (DMBD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10943))

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Abstract

The negative survey is an emerging method of collecting sensitive information. It could obtain the distribution of sensitive information while preserving the personal privacy. When collecting sensitive information, several questions are often provided together to the respondents. However, when reconstructing positive survey results of multiple questions, previous reconstruction methods have some shortcomings. In this paper, we propose a new reconstruction method for multi-question negative surveys. Experimental results show that our method could obtain more reasonable results.

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Acknowledgements

This work is partly supported by National Natural Science Foundation of China (No. 61175045).

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Authors and Affiliations

  1. Anhui Province Key Laboratory of Software Engineering in Computing and Communication, School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, Anhui, China

    Hao Jiang & Wenjiang Luo

Authors
  1. Hao Jiang
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  2. Wenjiang Luo
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Corresponding author

Correspondence to Wenjiang Luo .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Tongji University, Shanghai, China

    Qirong Tang

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Jiang, H., Luo, W. (2018). Multi-question Negative Surveys. In: Tan, Y., Shi, Y., Tang, Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science(), vol 10943. Springer, Cham. https://doi.org/10.1007/978-3-319-93803-5_47

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  • DOI: https://doi.org/10.1007/978-3-319-93803-5_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93802-8

  • Online ISBN: 978-3-319-93803-5

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