Assessing metadata privacy in neuroimaging
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917224354152448 |
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| author | Kibsgaard, Emilie Jwa, Anita Sue Markiewicz, Christopher J Gonzalez, David Rodriguez Pardo, Judith Sainz Poldrack, Russell A. Pernet, Cyril R. |
| author_facet | Kibsgaard, Emilie Jwa, Anita Sue Markiewicz, Christopher J Gonzalez, David Rodriguez Pardo, Judith Sainz Poldrack, Russell A. Pernet, Cyril R. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15278 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Assessing metadata privacy in neuroimaging Kibsgaard, Emilie Jwa, Anita Sue Markiewicz, Christopher J Gonzalez, David Rodriguez Pardo, Judith Sainz Poldrack, Russell A. Pernet, Cyril R. Other Quantitative Biology Cryptography and Security Computers and Society Image and Video Processing 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. |
| title | Assessing metadata privacy in neuroimaging |
| topic | Other Quantitative Biology Cryptography and Security Computers and Society Image and Video Processing |
| url | https://arxiv.org/abs/2509.15278 |