Secure and Distributed Access Control for Dynamic Pervasive Edge Computing Services
Bibliographic record
Abstract
Pervasive edge computing (PEC) integrates the re-sources of peer devices at the network edge to serve users' latency-sensitive computation needs. Due to the high dynamics of the PEC environment, it is very challenging to achieve efficient service access control of edge servers and users without an “always-online” centralized server. In this paper, we propose a secure, efficient, and distributed service access control frame-work (SE-DAC) in the PEC environment. Specifically, SE-DAC extends the key-aggregate cryptosystem to achieve batch service authorization, where the service provider aggregates the access keys of different services to produce a constant-size aggregate key for the edge servers. Meanwhile, user authentication tasks are delegated to the edge servers by integrating secret sharing. The mutual authentication between the edge servers and the users is based on zero-round trip communication, such that the communication bandwidth cost is low. In addition, the service provider can efficiently revoke the authorization of the dropout or compromised edge servers in response to the dynamics of the PEC environment. Finally, we conduct numerical analysis and experiments to demonstrate that SE-DAC is highly computational efficient on service authorization, authentication, and revocation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".