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Reconstructing Positive Surveys from Negative Surveys with Background Knowledge

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Data Mining and Big Data (DMBD 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9714))

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Abstract

Negative Survey is a promising technique for collecting sensitive data. Using the negative survey, useful aggregate information could be estimated, while protecting personal privacy. Previous work mainly focuses on improving the general model of the negative survey without considering background knowledge. However, in real-world applications, data analysts usually have some background knowledge on the surveys. Therefore, in this paper, for the first time, we study the usage of background knowledge in negative surveys, and propose a method for accurately reconstructing positive surveys with background knowledge. Moreover, we propose a method for evaluating the dependable level of the positive survey reconstructed with background knowledge. Experimental results show that more reasonable and accurate positive surveys could be obtained using our methods.

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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. School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, Anhui, China

    Dongdong Zhao, Wenjian Luo & Lihua Yue

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

    Dongdong Zhao, Wenjian Luo & Lihua Yue

Authors
  1. Dongdong Zhao
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  2. Wenjian Luo
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  3. Lihua Yue
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Corresponding author

Correspondence to Wenjian Luo .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Xi'an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

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© 2016 Springer International Publishing Switzerland

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Zhao, D., Luo, W., Yue, L. (2016). Reconstructing Positive Surveys from Negative Surveys with Background Knowledge. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2016. Lecture Notes in Computer Science(), vol 9714. Springer, Cham. https://doi.org/10.1007/978-3-319-40973-3_9

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  • DOI: https://doi.org/10.1007/978-3-319-40973-3_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40972-6

  • Online ISBN: 978-3-319-40973-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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