Social Work

Releasing survey microdata with exact cluster locations and additional privacy safeguards

T. Koebe, A. Arias-salazar, et al.

This innovative research by Till Koebe, Alejandra Arias-Salazar, and Timo Schmid showcases a groundbreaking approach to data anonymization. By releasing original and synthetic microdata, they significantly reduce re-identification risks while enhancing the utility of household survey data. Discover how this method transforms access to critical information without compromising privacy!

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Playback language: English

Index

Abstract

Household survey programs often publish georeferenced microdata, but anonymization methods like location obfuscation hinder data augmentation with local auxiliary information. This paper proposes an alternative: releasing two datasets – (1) original microdata without geographic identifiers for non-representative results and (2) synthetic microdata with original cluster locations. Experiments using 2011 Costa Rican census data and satellite information show this strategy reduces re-identification risk by 60-80%, even with multiple disclosed attributes, while maintaining data utility.

Publisher

Humanities & Social Sciences Communications

Published On

May 09, 2023

Authors

Till Koebe, Alejandra Arias-Salazar, Timo Schmid

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