Outsourcing Attribute-Based Encryption to Enhance IoT Security and Performance
Bibliographic record
Abstract
As the adoption of Internet of Things (IoT) systems, particularly those integrated with cloud technology, continues to expand, ensuring data security and privacy while maintaining optimal performance becomes increasingly challenging. Complex encryption algorithms, when run on IoT devices with limited resources, can significantly hinder processing speed and resource efficiency. This paper introduces an innovative Attribute-Based Encryption (ABE) framework that offloads computationally intensive cryptographic operations to a proxy server. This approach alleviates the computational strain on resource-constrained IoT devices, allowing them to efficiently handle encryption and decryption tasks despite their limited processing power, memory, and battery life. Additionally, we present a robust security model that ensures the privacy and integrity of data in IoT environments, in line with the requirements of ABE. We conduct an extensive performance analysis, evaluating key metrics such as execution time, ciphertext size, and memory usage, demonstrating that our proposed scheme surpasses existing state-of-the-art methods in efficiency. The primary contributions of this work include the development of a lightweight ABE offloading framework, the creation of a strong security model, and a thorough performance assessment that highlights the scheme’s efficiency and practicality for real-world IoT 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".