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

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  • Loukides, Grigorios
  • Lomax, Nik

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

  • Loukides, Grigorios & Lomax, Nik, 2021. "Privacy-preserving data publishing through anonymization, statistical disclosure control, and de-identification," OSF Preprints 2fvj7_v1, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:2fvj7_v1
    DOI: 10.31219/osf.io/2fvj7_v1
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