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Privacy-preserving data publishing through anonymization, statistical disclosure control, and de-identification

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  • Lomax, Nik
  • Loukides, Grigorios

Abstract

Recent developments in information technology allow the collection of massive amounts of data about individuals. These data capture a multitude of activities, characteristics, and aspects of the life of individuals, ranging from demographic, to financial and to health information. The use of the collected data is a valuable source for analyses, ranging from answering statistical (aggregate) queries to building statistical models for prediction and classification. However, there are considerable concerns regarding violations of personal privacy and misuse of the collected data. This paper provides an overview of methodological developments in the area of privacy-preserving data publishing, focusing on data anonymization and statistical disclosure control methods.

Suggested Citation

  • Lomax, Nik & Loukides, Grigorios, 2021. "Privacy-preserving data publishing through anonymization, statistical disclosure control, and de-identification," OSF Preprints 2fvj7, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:2fvj7
    DOI: 10.31219/osf.io/2fvj7
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    References listed on IDEAS

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    1. Amang Sukasih & Donsig Jang & David Edson, 2011. "Using Tau-Argus and sdcTable to Conduct Secondary Cell Supression for Linked Tables," Mathematica Policy Research Reports 2ad17bbc9add4a35b75904718, Mathematica Policy Research.
    2. Krishnamurty Muralidhar & Rathindra Sarathy, 2006. "Data Shuffling--A New Masking Approach for Numerical Data," Management Science, INFORMS, vol. 52(5), pages 658-670, May.
    3. repec:mpr:mprres:7106 is not listed on IDEAS
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