Bibliographic record
Abstract
Many models, methods, techniques, and systems have been developed to preserve the integrity of data and guarantee an acceptable level of security over networks. Protection from illegitimate data access and control of information flow are two main goals. This paper presents new techniques that address two main issues: information protection at various levels of granularity and data flow control We first investigate challenges and limits of established access control models regarding flow control. We then introduce a new flow control model based on granularity, the GBFC. GBFC is capable of guaranteeing flow control under reasonable assumptions. In addition, it offers advantages such as adaptability, full control, reliability and compatibility amongst others. Essentially, in GBFC classified information at suitable levels of granularity is accessible through references and information flow control is applied on the references. We also introduce the concepts of views for information access and Noise Injection that represent building blocks for the Granularity Based Flow Control. With noise injection, a document can be transformed into different views to erase or replace protected information and this transformation can be made almost undetectable to the unauthorized reader. Therefore, inference can be made much more difficult with this method. The GBFC model is intended to complement, rather than replace, existing access control methods.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".