Data Management for Future Wireless Networks: Architecture, Privacy Preservation, and Regulation
Bibliographic record
Abstract
Next-generation wireless networks (NGWN) aim to support diversified smart applications that require frequent data exchanges and collaborative data processing among multiple stakeholders. Data management (DM), including data collection, storage, sharing, and computation, plays an essential role in empowering NGWN. However, DM for NGWN faces two significant challenges: stakeholders' data cannot be easily managed across different trust domains under a distributed network architecture; and privacy preservation requirements of personal data become more rigorous under new privacy regulations. To explore possible solutions to address the challenges, we first investigate the state-of-the-art architecture designs for DM and emphasize advantages of a blockchain-based DM architecture. Then we summarize existing privacy-preserving techniques in terms of advantages and challenges when being applied to DM. In addition, we review recent privacy regulations with their impacts on DM and discuss the existing solutions with privacy regulation compliance based on blockchain. Finally, we identify further research directions for achieving DM with privacy preservation.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.002 | 0.005 |
| 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".