Performance Evaluation of CP-ABE Schemes under Constrained Devices
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
Recently, Internet of Things devices (IoT) have become a hot spot for researchers. Their industrial importance is growing exponentially day after day. Statistics show that the number of IoT devices will reach fifty billions by 2020. In addition, IoT applications are backed through the Cloud where data is stored and processed by gigantic processing systems. However, since the Cloud is honest but curious, sensitive information belonging to the IoT devices owners might be accessed and used beyond the intended purpose. Therefore, data privacy on Cloud servers should be preserved, which means that they should not reveal any piece of personally identifiable information (PII). Attribute Based Encryption (ABE) is a new form of public key encryption. ABE is a good candidate to achieve privacy and fine-grained access control for IoT applications running on Cloud servers. However, performing all the related cryptographic operations on such devices is practically infeasible because of their resource constraints. For alleviating all the computation burden on these resource-limited devices, several schemes have been proposed. In this paper, we investigate different ABE schemes, we implement and provide a performance evaluation in order to compare two relevant CP-ABE schemes: a fully encryption Vs a delegation based alternative.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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