Lightweight Broadcast Authentication Protocol for Edge-Based Applications
Bibliographic record
Abstract
In this article, we propose a lightweight authentication protocol that provides forward secrecy for edge-based applications. Motivated by the general consensus that centralized authentication solutions are not suitable for an expanding Internet of Things (IoT), our edge-based authentication reduces latency for critical applications, lowers cloud dependency, and employs cryptographic primitives, which are efficiently implemented on resource-constrained low-end devices. Moreover, the edge entity broadcast messages using session keys that are derived securely from a hash function. The protocol utilizes hash chains and authenticated encryption which makes it resilient to quantum attacks. Moreover, entities are not required to hold a permanent master key, and all session keys are derived securely from a hash function. As a use case, we present a smart emergency system where an edge application broadcasts alert messages for individual responder groups when specific events occur. We formally define and prove the main security properties of our protocol, and compare it to other lightweight protocols in terms of security and performance. The computational complexity of our protocol comprises of three decryption operations, two HMAC, and five hash computations. The required storage for each node is 96 B and the communication overhead is only 56 B per session.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".