A new perspective of privacy protection: Unique distinct l-SR diversity
Bibliographic record
Abstract
More and more public data sets which contain information about individuals are published in recent years. The urgency to reduce the risk of privacy disclosure from such data sets makes the approaches of privacy protection for data publishing widely employed. There are two popular models for privacy protection: k-anonymity and l-diversity. k-anonymity focuses on reducing the probability of identifying a particular person, which requires that each equivalence class (a set of records with the same identifier attributes) contains at least k records. l-diversity concentrates on reducing the inference from released sensitive attributes. It requires that each equivalence class has at least l “well-represented” sensitive attribute values. In this study, we view the privacy protection problem in a brand new perspective. We proposed a new model, Unique Distinct l-SR diversity based on the sensitivity of private information. Also, we presented two performance measures to evaluate how much sensitive information can be inferred from an equivalence class. l-SR diversity algorithm was implemented to achieve Unique Distinct l-SR diversity. We tested l-SR diversity on one benchmark data set and two synthetic data sets, and compared it with other l-diversity algorithms. The results show that our algorithm achieved better performance on minimizing inference of sensitive information and reached the comparable generalization data quality compared with other data publishing algorithms.
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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 | 0.018 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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".