Information preserving regression-based tools for statistical disclosure control

Langsrud, Øyvind

doi:10.1007/s11222-018-9848-9

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…27 There are many trade-offs between these techniques. 28,29 In the case of record-level data release, applying differential privacy would require employing a large amount of noise to obtain a meaningful privacy guarantee. As a result, the analytical utility of the output would be poor.…”

Section: Discussionmentioning

confidence: 99%

A proposed de-identification framework for a cohort of children presenting at a health facility in Uganda

Mawji

Longstaff

Trawin

et al. 2022

Preprint

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Data sharing has enormous potential to accelerate and improve the accuracy of research, strengthen collaborations, and restore trust in the clinical research enterprise. Nevertheless, there remains reluctancy to openly share raw datasets, in part due to concerns regarding research participant confidentiality and privacy. Statistical data de-identification is an approach that can be used to preserve privacy and facilitate open data sharing. We have proposed a standardized framework for the de-identification of data generated from cohort studies in children in a low-and-middle income country. Variables were labeled as direct and quasi-identifiers based on conditions of replicability, distinguishability, and knowability with consensus from two independent evaluators. Direct identifiers were removed from the dataset, while a statistical risk-based de-identification approach using the k-anonymity model was applied to quasi-identifiers. Qualitative assessment of the level of privacy invasion associated with data set disclosure was used to determine an acceptable re-identification risk threshold, and corresponding k-anonymity requirement. A de-identification model using generalization, followed by suppression was applied using a logical stepwise approach to achieve k-anonymity. The utility of the de-identified data was demonstrated using a typical clinical regression example. The de-identified dataset was published on the Pediatric Sepsis Data CoLaboratory Dataverse which provides moderated data access. Researchers are faced with many challenges when providing access to clinical data. We provide a standardized de-identification framework that can be adapted and refined based on specific context and risks. This process will be combined with moderated access to foster coordination and collaboration in the clinical research community.

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Section: Discussionmentioning

confidence: 99%

A proposed de-identification framework for a cohort of children presenting at a health facility in Uganda

Mawji

Longstaff

Trawin

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…K-anonymity is used to prevent re-identification of individuals made possible by record linking attacks while the differential privacy provides a probabilistic guarantee that the inclusion of an individual in a data set will not alter the outcome of a query to that data sets [27]. There are many trade-offs between these techniques [28,29]. In the case of record-level data release, applying differential privacy would require employing a large amount of noise to obtain a meaningful privacy guarantee.…”

Section: Plos Digital Healthmentioning

confidence: 99%

A proposed de-identification framework for a cohort of children presenting at a health facility in Uganda

Mawji

Longstaff²,

Trawin³

et al. 2022

PLOS Digit Health

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Data sharing has enormous potential to accelerate and improve the accuracy of research, strengthen collaborations, and restore trust in the clinical research enterprise. Nevertheless, there remains reluctancy to openly share raw data sets, in part due to concerns regarding research participant confidentiality and privacy. Statistical data de-identification is an approach that can be used to preserve privacy and facilitate open data sharing. We have proposed a standardized framework for the de-identification of data generated from cohort studies in children in a low-and-middle income country. We applied a standardized de-identification framework to a data sets comprised of 241 health related variables collected from a cohort of 1750 children with acute infections from Jinja Regional Referral Hospital in Eastern Uganda. Variables were labeled as direct and quasi-identifiers based on conditions of replicability, distinguishability, and knowability with consensus from two independent evaluators. Direct identifiers were removed from the data sets, while a statistical risk-based de-identification approach using the k-anonymity model was applied to quasi-identifiers. Qualitative assessment of the level of privacy invasion associated with data set disclosure was used to determine an acceptable re-identification risk threshold, and corresponding k-anonymity requirement. A de-identification model using generalization, followed by suppression was applied using a logical stepwise approach to achieve k-anonymity. The utility of the de-identified data was demonstrated using a typical clinical regression example. The de-identified data sets was published on the Pediatric Sepsis Data CoLaboratory Dataverse which provides moderated data access. Researchers are faced with many challenges when providing access to clinical data. We provide a standardized de-identification framework that can be adapted and refined based on specific context and risks. This process will be combined with moderated access to foster coordination and collaboration in the clinical research community.

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Synthetic Decimal Numbers as a Flexible Tool for Suppression of Post-published Tabular Data

Langsrud

Bøvelstad²

2022

Lecture Notes in Computer Science

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Information preserving regression-based tools for statistical disclosure control

Cited by 6 publications

References 22 publications

A proposed de-identification framework for a cohort of children presenting at a health facility in Uganda

A proposed de-identification framework for a cohort of children presenting at a health facility in Uganda

A proposed de-identification framework for a cohort of children presenting at a health facility in Uganda

Synthetic Decimal Numbers as a Flexible Tool for Suppression of Post-published Tabular Data

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