A Distributed Trust Management Scheme in the Pervasive Computing Environment
Bibliographic record
Abstract
Pervasive computing allows a seamless interaction among users, devices, and applications, anytime and anywhere. Yet portable devices in pervasive computing are mainly powered by batteries and have limited computational and communication capability. Thus the open and dynamic environment in pervasive computing raises challenges in security and trust management. Without trust, pervasive devices cannot cooperate effectively, and the deployment of pervasive computing systems will be restricted to specific application scenarios. The traditional centralized security management schemes are not directly applicable in pervasive computing environments. Moreover, existing user authentication and access control schemes are inadequate to ensure security in pervasive computing. To overcome the limitation of centralized schemes, we need a distributed solution. In this paper, we propose a distributed trust management scheme to ensure security in pervasive computing environments. The main contributions of this paper are: (1) the employment of a simple, distributed trust computation and maintenance mechanism to reduce communication and computational overhead without compromising security; (2) the building of an aggregate trust metric that is based on direct observation and indirect observations obtained from neighbors' recommendations. The scheme gives more weight to direct observations and less weight to indirect observations. Every device computes and updates the trust value periodically in a distributed fashion. However, the exchange of trust information is carried out on demand to reduce communication overhead. The operation of the proposed scheme with varying parameter settings is illustrated, using an analytical approach.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".