Drive More Effective Data-Based Innovations: Enhancing the Utility of Secure Databases
Bibliographic record
Abstract
Databases play a central role in evidence-based innovations in business, economics, social, and health sciences.In modern business and society, there are rapidly growing demands for constructing analytically valid databases that also are secure and protect sensitive information in order to meet customer and public expectations, to minimize financial losses, and to comply with privacy regulations and laws.We propose new data perturbation and shuffling (DPS) procedures, named MORE, for this purpose.As compared with existing DPS methods, MORE can substantially increase the utility of secure databases without increasing disclosure risk.MORE is capable of preserving important nonmonotonic relationships among attributes, such as the inverted-U relationship between competition and innovation.Maintaining such relationships is often the key to determining optimal levels of policy and managerial interventions.MORE does not require data to be of particular types or have particular distributional shapes.Instead, it provides unified, flexible, and robust algorithms to mask general types of confidential variables with arbitrary distributions, thereby making it suitable for general-purpose data masking.Since MORE nests the commonly used generalized linear models as special cases, a much wider range of statistical analyses can be conducted using the secure databases with results similar to those using the original databases.Unlike existing DPS approaches which typically require a joint model for all variables, MORE requires no modeling of nonconfidential variables, and thus further increases the robustness of secure databases.Evaluation of MORE through Monte Carlo simulation studies and empirical applications demonstrates that it performs better than existing data masking methods.
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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 | 0.024 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".