Secure and Distributed Access Control for Dynamic Pervasive Edge Computing Services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it