评估脑影像数据元信息隐私，发现人口统计变量是主要风险点。
Assessing metadata privacy in neuroimaging
- 用 metaprivBIDS 工具量化元数据隐私，采用 k-匿名等五种指标分析
- 近所有数据集存在隐私漏洞，但严重威胁罕见，临床评分风险低
- 年龄、性别、种族等人口信息最易导致重识别，适合数据共享团队参考
为实现科研数据共享而不造成伤害，需关注隐私风险。我们分析了 OpenNeuro 上多个跨生命周期（儿童至老年）的神经影像数据集元信息，涵盖有无临床诊断的受试者及关联临床评分。利用 metaprivBIDS 工具（支持 BIDS 标准的 tsv/json 文件），计算并报告 k-anonymity、k-global、l-diversity、SUDA、PIF 等隐私指标。结果显示，整体隐私保护良好，严重漏洞罕见。但几乎所有数据集均存在隐患。值得注意的是，临床评分数据重识别风险较低；而年龄、出生性别、性取向、种族、收入和地理信息是主要隐私威胁。本文提出具体措施以降低风险，推动更安全的数据共享实践。
原文摘要 · Abstract (English)
The ethical and legal imperative to share research data without causing harm requires careful attention to privacy risks. While mounting evidence demonstrates that data sharing benefits science, legitimate concerns persist regarding the potential leakage of personal information that could lead to reidentification and subsequent harm. We reviewed metadata accompanying neuroimaging datasets from heterogeneous studies openly available on OpenNeuro, involving participants across the lifespan, from children to older adults, with and without clinical diagnoses, and including associated clinical score data. Using metaprivBIDS (https://github.com/CPernet/metaprivBIDS), a software application for BIDS compliant tsv/json files that computes and reports different privacy metrics (k-anonymity, k-global, l-diversity, SUDA, PIF), we found that privacy is generally well maintained, with serious vulnerabilities being rare. Nonetheless, issues were identified in nearly all datasets and warrant mitigation. Notably, clinical score data (e.g., neuropsychological results) posed minimal reidentification risk, whereas demographic variables: age, sex assigned at birth, sexual orientations, race, income, and geolocation, represented the principal privacy vulnerabilities. We outline practical measures to address these risks, enabling safer data sharing practices.
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