POWoT : a Privacy Ontology for the Web of Things
Bibliographic record
Abstract
The W3C Web of Things (WoT) is a set of Web standards intended to enable interoperability between IoT platforms. The idea consists to propose WoT Thing Description (TD) specification that describes the metadata and interaction's interfaces of Things (IoT devices or services) at an appropriate level of abstraction based on a small vocabulary that makes it possible both to integrate diverse devices and to allow diverse applications to interoperate. TDs are made available on the network encoded in a JSON format that also allows JSON-LD processing to provide a powerful foundation to represent knowledge about Things in machine-understandable way. TD instances consist mainly of metadata about the functional part of the Thing itself, i.e., a set of interaction capabilities that indicate how the Thing can be used, possible input/output data schema, binding protocols, linking to other TD as well as the followed security schemes. Even if the latter is important to minimize some security risks in the WoT, it unfortunately does not fully take privacy aspects into consideration. We propose in this article an ontology-based privacy model to minimize privacy violation risks in the IoT context in which connected devices are increasingly able to access and manage personal data when monitoring human activities. We propose a WoT privacy ontology to enrich the TD representation with an information model allowing to specify the terms and conditions of private information management in the WoT. We then integrate this non-functional aspect in TD logic-based matchmaking and discovery process in TD Directories.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".