Linked Closed Data Using PKI: A Case Study on Publishing and Consuming data in a Forensic Process
Bibliographic record
Abstract
The main aim of the Linked Open Data (LOD) project is to publish data publicly without access restriction in order to be consumed upon Unified Resource Identifier (URI) resolution. The latter provides more description about the resources being represented through the resolvability and discoverability of more others resources. Sometimes, data/resources need to undergo an access restriction to be consumed only on a small scale for keeping its confidentiality. However, while the power of the LOD resides in the resolvability of more URIs related to the resources in hand, a curious question imposes itself: how can we achieve a compromise between URI resolvability and access restriction? This paper discusses how the represented data cam be secured. It illustrates how the Public Key Infrastructure (PKI) can be applied to restrict the access to confidential resources of represented data being published using the Linked Data Principles (LDP), while maintaining the resolvability of such restricted resources. This brings out a new era of research related to the counter part of LOD, a research topic called the Linked Closed Data (LCD). A good example to elaborate this compromise question is a case study retrieved from the Cyber Forensics (CF) field where the tangible Chain of Custody (CoC) is represented using the LDP to exploit the resolvability feature of such principles on different resources of the Electronic-CoC (e-CoC). The latter should also obey an access restriction in order to be shared only between role players who published the data and juries who are going to consume it.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".