On Data Distortion for Privacy Preserving Data Mining
Bibliographic record
Abstract
Because of the increasing ability to trace and collect large amount of personal information, privacy preserving in data mining applications has become an important concern. Data perturbation is one of the well known techniques for privacy preserving data mining. The objective of these data perturbation techniques is to distort the individual data values while preserving the underlying statistical distribution properties. Theses data perturbation techniques are usually assessed in terms of both their privacy parameters as well as its associated utility measure. While the privacy parameters present the ability of these techniques to hide the original data values, the data utility measures assess whether the dataset keeps the performance of data mining techniques after the data distortion. In this paper, we investigate the use of truncated non-negative matrix factorization (NMF) with sparseness constraints for data perturbation.
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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 | 0.026 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".