Enhancing Privacy in Wearable IoT through a Provenance Architecture
Bibliographic record
Abstract
The Internet of Things (IoT) is inspired by network interconnectedness of humans, objects, and cloud services to facilitate new use cases and new business models across multiple enterprise domains including healthcare. This creates the need for continuous data streaming in IoT architectures which are mainly designed following the broadcast model. The model facilitates IoT devices to sense and deliver information to other nodes (e.g., cloud, physical objects, etc.) that are interested in the information. However, this is a recipe for privacy breaches since sensitive data, such as personal vitals from wearables, can be delivered to undesired sniffing nodes. In order to protect users’ privacy and manufacturers’ IP, as well as detecting and blocking malicious activity, this research paper proposes privacy-oriented IoT architecture following the provenance technique. This ensures that the IoT data will only be delivered to the nodes that subscribe to receive the information. Using the provenance technique to ensure high transparency, the work is able to provide trace routes for digital audit trail. Several empirical evaluations are conducted in a real-world wearable IoT ecosystem to prove the superiority of the proposed work.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".