Bibliographic record
Abstract
The concept of privacy is expressed as release of information in a controlled way. Privacy could also be defined as privacy decides what type of personal information should be released and which group or person can access and use it. Privacy Preserving Data Publishing (PPDP) is a way to allow one to share anonymous data to ensure protection against identity disclosure of an individual. Data anonymization is a technique for PPDP, which makes sure the published data, is practically useful for processing (mining) while preserving individuals sensitive information. Most works reported in literature on privacy preserving data publishing for classification task handle numerical data. However, most real life data contains both numerical and non-numerical data. Another shortcoming is that use of distributed model called Secure Multiparty Computation (SMC). For this research, a centralized model is used for independent data publication by a single data owner. The key challenge for PPDP is to ensure privacy as well as to keep the data usable for research. Differential privacy is a technique that ensures the highest level of privacy for a record owner while providing actual information of the data set. The aim of this research is to develop a framework that satisfies differential privacy standards and to ensure maximum data usability for a classification tasks such as patient data classification in terms of blood pressure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".