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  • 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. (xsd:string)
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  • (Eurobarometer) (xsd:string)
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  • Eurobarometer-Bibliography (xsd:string)
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  • 2024 (xsd:gyear)
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  • 2024 (xsd:gyear)
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  • 123 (xsd:string)
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  • english (xsd:string)
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  • 2013-1631 ()
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  • When to use the k-rule? - Criteria for managing uniqueness and de-anonymization risk in social science survey data (xsd:string)
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  • article (xsd:string)
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  • Bibsonomy (xsd:string)
  • In Transactions On Data Privacy, 17(3), 123-146, 2024 (xsd:string)
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  • EB - Standard and Special Eurobarometer (xsd:string)
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  • 2024 (xsd:string)
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  • 146 (xsd:string)
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  • 17 (xsd:string)