Protecting Privacy in Digital Records: The Potential of Privacy-Enhancing Technologies
Bibliographic record
Abstract
With increased concerns about data protection and privacy over the past several years, and concomitant introduction of regulations restricting access to personal information (PI), archivists in many jurisdictions now must undertake ‘sensitivity reviews’ of archival documents to determine whether they can make those documents accessible to researchers. Such reviews are onerous given increasing volume of records and complex due to how difficult it can be for archivists to identify whether records contain PI under the provisions of various laws. Despite research into the application of tools and techniques to automate sensitivity reviews, effective solutions remain elusive. Not yet explored as a solution to the challenge of enabling access to archival holdings subject to privacy restrictions is the application of privacy-enhancing technologies (PETs) —a class of emerging technologies that rest on the assumption that a body of documents is confidential or private and must remain so. While seemingly being counterintuitive to apply PETs to making archives more accessible, we argue that PETs could provide an opportunity to protect PI in archival holdings whilst still enabling research on those holdings. In this article, to lay a foundation for archival experimentation with use of PETs, we contribute an overview of these technologies based on a scoping review and discuss possible use cases and future research directions.
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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 | 0.044 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.016 | 0.039 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".