Verification of data pattern for interactive privacy preservation model
Bibliographic record
Abstract
The research problem of privacy-preserving data publishing is to release microdata in an aggregated form using distinguished techniques that will effectively conceal sensitive and private information but can be used by external users to exercise data mining. These techniques are often studied in interactive and non-interactive settings. While non-interactive setting mainly deals with the data publication using anonymization or noise addition approaches, interactive models are based on noisy response of queries. Most of the data pattern verification and classification accuracy determination approaches exist for non-interactively published microdata. In this paper, we verify the data pattern and determine classification accuracy on an interactive privacy preservation model called differential privacy. The contributions of this paper are: (1) We present a concise literature review of non-interactive and interactive models and technologies. (2) We propose an approach of retrieving information along with investigating, understanding and comparing the data classification accuracy experimentally on Privacy Integrated Queries. (3) We verify data pattern by comparing the correlation and classification accuracy of the differentially private data with non-interactive k-anonymous data.
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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 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 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".