EPS: An Efficient and Privacy-Preserving Service Searching Scheme for Smart Community
Bibliographic record
Abstract
Smart community leverages information and communications technology to improve the quality of life in terms of education, health care, and government services. In smart community, residents manage their home appliances to cooperate on stabilizing renewable power supply, energy saving, and information communications. In this paper, we propose an efficient and privacy-preserving service searching scheme (EPS) for smart community to enable residents to receive some Internet bandwidth from cooperative nearby homes so as to obtain pervasive Internet access at the cheap cost. Specifically, the EPS enables a resident to send a service request to nearby homes, and the latter responds the request with either uploading data via Internet connection or forwarding data to other homes via WiFi. As the Internet and WiFi bandwidth for homes is limited, the homes assign residents with different priorities and prefer to serve residents with high priorities. The priority is determined by a proximity score between residents and home owners, and the identity information is not disclosed in the calculation process. Moreover, the EPS preserves the location privacy of residents by adopting the multiple pseudonym techniques. Detailed privacy analyses in terms of the identity privacy and the location privacy are provided. In addition, the communication efficiency is validated through extensive simulations.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".