An ontology driven privacy framework for collaborative working environments
Bibliographic record
Abstract
The ability to collaborate has always been vitally important to organisations. With the availability of current networking and computing power, the creation of collaborative working environments (CWEs) has allowed for this process to occur anytime over any geographical distance. While the strength of a CWE is its ability to allow for the exchange of information freely between collaborators, this opens the users up to a possible loss of privacy. In this work, the issue of protecting privacy while collaborating is discussed. To address the privacy concerns that are raised, a privacy framework is presented. This framework contains a generic privacy ontology that allows the framework to adapt to any domain, a reasoning engine that infers who has access to what information according to which privacy rules, and a collaborative privacy manager (CPM) which can make decisions and assist collaborating users with their privacy protection. The main objective of this work is to present the generic ontology of privacy and to highlight tasks the CPM performs within a CWE. This work has been deployed through a prototype tool that demonstrates a scenario between a hospital and a university.
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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| 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".