An efficient and secure scheme for smart home communication using identity-based signcryption
Bibliographic record
Abstract
Securing communication between users and devices is an important aspect of Internet of Things applications. Although a number of cryptographic schemes have been proposed for securing communication among IoT devices, the ability to handle such schemes, especially with devices that have constrained computing resources, is difficult. Thus, there is a need for a secure and efficient scheme that will protect connections between devices with limited capabilities. For overcoming such constraints, many symmetric and asymmetric cryptographic techniques have been proposed. However, not all of the available cryptographic mechanisms can satisfy all of the security goals. Fortunately, there is a single mechanism that can provide combined security goals with low cost in terms of computation and communication overhead in addition to memory requirement. This technique, known as signcryption, can more efficiently satisfy authentication, integrity and confidentiality than combining encryption and signature schemes. This paper presents an identity-based signcryption scheme for smart home communication. Analysis and evaluation show that, in addition to efficiently providing authentication, the proposed scheme provides integrity and confidentiality as well as the ability to protect communication between devices against possible attacks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".