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A framework for utility enhanced incomplete microdata anonymization

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Abstract

Incomplete microdata, i.e., microdata with missing value, is very common in real-world datasets. However, existing anonymization techniques, which were developed for complete datasets, suffer from serious information loss on incomplete microdata, due to the missing value pollution. In this paper, we propose a framework for utility enhanced anonymization of incomplete microdata to address this issue. First, we study the properties of missing value pollution on generalization. Guided by these properties, we develop two top-down anonymization algorithms to preserve data utility on incomplete microdata. Extensive experiments on real-world datasets show that our techniques outperform the state-of-the-art techniques in terms of information loss and missing value pollution.

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Notes

  1. Mondrian, Enhanced Mondrian and semi-partition.

  2. Downloadable at http://archive.ics.uci.edu/ml/datasets/Adult.

  3. Downloadable at https://sites.google.com/site/informsdataminingcontest/.

  4. According to the documents provided by UCI and INFORMS, ‘?’ in Adult data and -1, -7, -8, -9 in INFORMS are considered as missing values.

  5. We assume age range is [1, 100], and Zipcode range is [10001, 50000].

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Acknowledgements

This work is supported by National Natural Science Foundation of China under Grants No. 61572130, 61632008, 61320106007, 61502100 and 61402104, Jiangsu Provincial Natural Science Foundation under Grants BK20150628, BK20140648 and BK20150637, Jiangsu Provincial Key Technology R&D Program under Grant BE2014603, Qing Lan Project of Jiangsu Province, Jiangsu Provincial Key Laboratory of Network and Information Security under Grant BM2003201, and Key Laboratory of Computer Network and Information Integration of Ministry of Education of China under Grant 93K-9.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast University, Nanjing, China

    Qiyuan Gong, Ming Yang, Wenjia Wu & Junzhou Luo

  2. Science and Technology on Communication Security Laboratory, Chengdu, China

    Qiyuan Gong, Ming Yang & Zhouguo Chen

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  1. Qiyuan Gong
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  2. Ming Yang
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  3. Zhouguo Chen
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  4. Wenjia Wu
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  5. Junzhou Luo
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Correspondence to Junzhou Luo.

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Gong, Q., Yang, M., Chen, Z. et al. A framework for utility enhanced incomplete microdata anonymization. Cluster Comput 20, 1749–1764 (2017). https://doi.org/10.1007/s10586-017-0795-6

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