Bibliographic record
Abstract
Newly published data, when combined with existing public knowledge, allows for complex and sometimes unintended inferences. We propose semi-automated tools for detecting these inferences prior to releasing data. Our tools give data owners a fuller understanding of the implications of releasing data and help them ad-just the amount of data they release to avoid unwanted inferences. Our tools first extract salient keywords from the pri-vate data intended for release. Then, they issue search queries for documents that match subsets of these key-words, within a reference corpus (such as the public Web) that encapsulates as much of relevant public knowl-edge as possible. Finally, our tools parse the documents returned by the search queries for keywords not present in the original private data. These additional keywords allow us to automatically estimate the likelihood of cer-tain inferences. Potentially dangerous inferences are flagged for manual review. We call this new technology Web-based inference control. The paper reports on two experiments which demonstrate early successes of this technology. The first experiment shows the use of our tools to automatically estimate the risk that an anonymous document allows for re-identification of its author. The second experiment shows the use of our tools to detect the risk that a doc-ument is linked to a sensitive topic. These experiments, while simple, capture the full complexity of inference de-tection and illustrate the power of our approach. 1
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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 | 0.014 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".