Strategies for Protecting Privacy in Open Data and Proactive Disclosure
Bibliographic record
Abstract
In this paper, the authors explore strategies for balancing privacy with transparency in the release of government data and information as part of the growing global open government movement. The issue is important because government data or information may take many forms, may contain many different types of personal information, and may be released in a range of contexts. The legal framework is complex: personal information is typically not released as open data or under access to information regimes; nevertheless, in some cases transparency requirements take precedence over the protection of personal information. The open courts principle, for example, places a strong emphasis on transparency over privacy. The situation is complicated by the availability of technologies that facilitate widespread dissemination of information and that allow for the searching, combining and mining of information in ways that may permit the reidentification of individuals even within anonymized data sets.\nThis paper identifies a number of strategies designed to assist in identifying whether government data sets or information contain personal information, whether it should be released notwithstanding the presence of the personal information, and what techniques might be used to minimize any possible adverse privacy impacts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.091 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.019 | 0.036 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".