A survey on statistical disclosure control and micro‐aggregation techniques for secure statistical databases
Bibliographic record
Abstract
Abstract This paper surveys the fields of Statistical Disclosure Control (SDC) and Micro‐Aggregation Techniques (MATs), which are both areas fundamental to the science of secure Statistical DataBases (SDBs). The paper is written from the perspective of a computer scientist with the hope that it will prove to be a source of reference material useful to researchers and practitioners in the field. The paper first introduces the concept ofSDCand describes the domain of its applications and the various data types that are currently used inSDBs. It then proceeds to focus on the family of micro‐data types inSDBs. At this juncture, we introduce the importance of the relevant measures, namely the metrics termed as the Information Loss (IL) and the Disclosure Risk (DR), after which we survey the various methods of resolving the conflicting goals that these metrics represent. Thereafter, the paper summarizes the perturbative and non‐perturbativeSDCmethods for micro‐data protection, and it focuses on the families ofMATs by formally stating the Micro‐Aggregation Problem and surveying it in a comprehensive manner. Apart from the paper including a historical view of the field ofMATs, it describes a broad selection of work that has been reported more recently. Indeed, we believe that this paper represents a complete overview of the state‐of‐the‐art techniques. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".