Bibliographic record
Abstract
The growth of the Internet has been accompanied by the growth of Internet services (e.g., e-commerce, e-health). This proliferation of services and the increasing attacks on them by malicious individuals have highlighted the need for service security. The security requirements of an Internet or Web service may be specified in a security policy. The provider of the service is then responsible for implementing the security measures contained in the policy. However, a service customer or consumer may have security preferences that are not reflected in the provider’s security policy. In order for service providers to attract and retain customers, as well as reach a wider market, a way of personalizing a security policy to a particular customer is needed. We derive the content of an Internet or Web service security policy and propose a flexible security personalization approach that will allow an Internet or Web service provider and customer to negotiate to an agreed-upon personalized security policy. In addition, we present two application examples of security policy personalization, and overview the design of our security personalization prototype.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.002 | 0.035 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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; both teacher heads agree on what is shown here.
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".