Security and Efficiency Enhanced Revocable Access Control for Fog-Based Smart Grid System
Bibliographic record
Abstract
With the popularity of smart grids, plentiful of smart devices have been put into use, such as smart meters and power assets. Due to limited computation capabilities and storage spaces of these devices, the collected data need to be “outsourced” towards the data server for processing and storage. The data owners, therefore, lose direct control over these “outsourced” data, leading to significant security issues of the users' data. In this paper, aiming at solving this problem, we propose a multi-authority Ciphertext Policy Attribute-based Encryption (CP-ABE) scheme with revocation for the fog-based smart grid system. Specifically, in order to achieve attribute revocation without requiring users to be always online, we use the DH (Diffie-Hellman) tree to distribute the group key statelessly, which also solves the problem of collusion attack initiated by revoked user and valid user. To improve security of our proposed scheme, we remove the trusted key authority (KA) by using a secure two-party computation (2PC) protocol between the KA and the cloud service provider to generate user private key. To improve efficiency of our proposed scheme, we combine user and attribute revocation, and outsource complex calculations to fog nodes. Furthermore, our proposed scheme uses attribute group key and leaf private key together to protect user proxy key, which reduces the storage overhead of the system and improves the security. Both security analysis and experimental results demonstrate that our proposed scheme can balance the security objectives with the efficiency.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".