PRIVACY AND ANONYMITY PROTECTION IN COMPUTATIONAL GRID SERVICES
Bibliographic record
Abstract
In computational grid computing, grid nodes spanning over several diverse computing resources belonging to heterogeneous administrative domains form the backbone of Virtual Enterprise [VE]. In order to offer service-on-demand, various service providers, requesters, brokers and administrators collaborate in request-response manner among each other in Service Oriented Virtual Enterprise through service registry, service discovery and service binding mechanisms. Security issues for integrated and collaborative sharing of computing resources across heterogeneous administrative domains are principal concern. At the same time, the privacy and anonymity are also of prime importance while communicating over publicly spanned network like web. The individual service providers or requesters may not reveal their true identity to one another for privacy needs. Also, computational grid services may be required to be availed anonymously within the grid framework to keep the personal sensitive information about the service requester protected. This paper focuses on the protection of privacy and anonymity of grid stakeholders in the service oriented computational grid framework. An extension of onion routing has been used with dynamic token exchange along with protection of privacy and anonymity of individual identity.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".