An Application Evaluation for Differentially Private Database Release Methods
Bibliographic record
Abstract
Privacy violations involving de-anonymizing personal identifiable information (PII) due to data mining or breach is a corporate domain concern. Therefore, maintaining individual data anonymity has been challenging due to de-anonymization strategies employed to mine personal data. Counteracting de-anonymization data mining techniques have been deployed where anonymous data is cross-referenced with related data sources to de-anonymize the data source. Subsequently, privacy-enhancing technologies [PETs] have been suggested and deployed in academic and professional to prevent de-anonymization when extracting information from large datasets. Differential privacy (DP) is among the preferred privacy commitments among contemporary privacy models because it guarantees the protection of user-sensitive data. This research evaluates three differentially private database release methods, kernel mean embedding (KME), the learning theory (LT) approach to non-interactive database privacy, and the statistical framework for (SF) differential privacy. This evaluation indicates limitations and provides applicability suggestions for each method to maintain data anonymity and measures to overcome the challenges. Our study provides practitioners with guidelines on the DP selection based on the data nature.
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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 | 0.019 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".