Preventing or utilising key escrow in identity-based schemes employed in mobile ad hoc networks
Bibliographic record
Abstract
Recently, Identity-Based Cryptography (IBC) schemes have been considered as a tool to secure Mobile Ad Hoc Networks (MANETs) due to the efficient key management of the schemes. In this work, we focus on the role of the Key Generation Centre (KGC) as a key escrow, a property that is inherent to all IBC schemes. We explore the special role of key escrow in MANETs and show that this role significantly differs from key escrows in other networks. We introduce two adversary models for dishonest KGCs in MANETs, including a new spy model where a KGC uses so-called spy nodes that record communications in the network and report them to the KGC. We discuss the two faces of key escrow in MANETs, where our analytical results show that in many MANET applications the KGC can be prevented from being a key escrow. On the other hand, the results of this paper illustrate how a KGC can utilise spy nodes to monitor nodes in a MANET, as needed in some applications.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".