Author manuscript, published in "Privacy, Security and Trust, Montreal: Canada (2011)" Safe Realization of the Generalization Privacy Mechanism *
Bibliographic record
Abstract
Abstract—An increasing number of surveys and articles highlight the failure of database servers to keep confidential data really private. Even without considering their vulnerability against external or internal attacks, mere negligences often lead to privacy disasters. The advent of powerful smart portable tokens, combining the security of smart card microcontrollers with the storage capacity of NAND Flash chips, introduces today credible alternatives to the systematic centralization of personal data on servers. Individuals can now store their personal data (e.g., their medical folder) in their own smart tokens, kept under their control, and never disclose in clear their private data to the outside untrusted world. However, this new opportunity of managing and protecting personal data conflicts with the objective of implementing knowledge-based decision making tools on top of centralized data. This paper precisely addresses this issue and proposes to adapt the traditional Generalization privacy mechanism to an environment composed of a large set of tamper-resistant smart portable tokens seldom connected to a highly available but untrusted infrastructure. This combination of hypothesis makes the problem fundamentally different from any previously studied privacy-preserving data publishing problem we are aware of. I.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.277 | 0.041 |
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".