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Volume 17 Issue 3

When to use the k-rule? - Criteria for managing uniqueness and de-anonymization risk in social science survey data

Anja Perry^(a),(*), Wolfgang Zenk-Möltgen^(a)

Transactions on Data Privacy 17:3 (2024) 123 - 146

Abstract, PDF

(a) GESIS - Leibniz Institute for the Social Sciences, Unter Sachsenhausen 6-8, 50667 Cologne, Germany.

e-mail:anja.perry @gesis.org; wolfgang.zenk-moeltgen @gesis.org

Abstract

Anonymization plays a large role for data sharing in the social sciences, where research subjects are often human. In this paper we are looking at k-anonymization, an anonymization strategy rarely used in the social sciences. This is due to high-dimensional socio-economic information necessary for social science research. Here, the k-rule is often too rigid and leads to information loss. We argue, however, that certain datasets need to be k-anonymized and suggest criteria to determine the need for this rule. We then apply our criteria to example datasets from a social science data archive. In doing so, we provide criteria for data curators to determine which level of anonymization to apply to data at hand and hands-on examples on how to apply them. We aim to improve workflows for data archives and support safe data sharing practices.

^* Corresponding author.

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