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<p>One of many challenges to open science is anonymization of personal data so that it may be shared. This paper presents a case study of the anonymization of a dataset containing cardio-respiratory fitness and commuting patterns for Slovenian school children. It evaluates three different anonymization tools, ARX, SDV, and SynDiffix. The fitness study was selected because its small size (N=713) and generally low statistical significance make it particularly challenging for data anonymization. Unlike most prior anonymization tool evaluations, this paper examines whether the scientific conclusions of the original study would have been supported by the anonymized datasets. It also considers the burden imposed on researchers using the tools both for data generation and data analysis.</p>

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Comparison of Three Anonymization Tools for a Health Fitness Study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月18日

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  • Paul Francis,
  • Gregor Jurak,
  • Bojan Leskošek,
  • Karen Otte,
  • Fabian Prasser

摘要

One of many challenges to open science is anonymization of personal data so that it may be shared. This paper presents a case study of the anonymization of a dataset containing cardio-respiratory fitness and commuting patterns for Slovenian school children. It evaluates three different anonymization tools, ARX, SDV, and SynDiffix. The fitness study was selected because its small size (N=713) and generally low statistical significance make it particularly challenging for data anonymization. Unlike most prior anonymization tool evaluations, this paper examines whether the scientific conclusions of the original study would have been supported by the anonymized datasets. It also considers the burden imposed on researchers using the tools both for data generation and data analysis.