Bibliographic record
Abstract
De-identifcation is a process that is applied to a dataset with the goal of preventing or limiting informational risks to individuals, protected groups, and establishments while still allowing for meaningful statistical analysis.Government agencies can use de-identifcation to reduce the privacy risk associated with collecting, processing, archiving, distributing, or publishing government data.Previously, NISTIR 8053, De-Identifcation of Personal Information [51], provided a survey of de-identifcation and re-identifcation techniques.This document provides specifc guidance to government agencies that wish to use deidentifcation.Before using de-identifcation, agencies should evaluate their goals for using de-identifcation and the potential risks that de-identifcation might create.Agencies should decide upon a de-identifcation release model, such as publishing de-identifed data, publishing synthetic data based on identifed data, or providing a query interface that incorporates de-identifcation.Agencies can create a Disclosure Review Board to oversee the process of de-identifcation.They can also adopt a de-identifcation standard with measurable performance levels and perform re-identifcation studies to gauge the risk associated with de-identifcation.Several specifc techniques for de-identifcation are available, including de-identifcation by removing identifers and transforming quasi-identifers and the use of formal privacy models.People performing de-identifcation generally use specialpurpose software tools to perform the data manipulation and calculate the likely risk of re-identifcation.However, not all tools that merely mask personal information provide suffcient functionality for performing de-identifcation.This document also includes an extensive list of references, a glossary, and a list of specifc de-identifcation tools, which is only included to convey the range of tools currently available and is not intended to imply a recommendation or endorsement by NIST.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".