Re-Encryption-Based Key Management Towards Secure and Scalable Mobile Applications in Clouds.
Bibliographic record
Abstract
Abstract—Cloud computing confers strong economic advantages, but many clients are reluctant to implicitly trust a third-party cloud provider. To address these security concerns, data may be transmitted and stored in encrypted form. Major challenges exist concerning the aspects of the generation, distribution, and usage of encryption keys in cloud systems, such as the safe location of keys, and serving the recent trend of users that tend to connect to contemporary cloud applications using resource-constrained mobile devices in extremely large numbers simultaneously; these characteristics lead to difficulties in achieving efficient and highly scalable key management. In this work, a model for key distribution based on the principle of dynamic data re-encryption is applied to a cloud computing system in a unique way to address the demands of a mobile device environment, including limitations on client wireless data usage, storage capacity, processing power, and battery life. The proposed cloud-based re-encryption model is secure, efficient, and highly scalable in a cloud computing context, as keys are managed by the client for trust reasons, processor-intensive data re-encryption is handled by the cloud provider, and key redistribution is minimized to conserve communication costs on mobile devices. A versioning history mechanism effectively manages keys for a continuously changing user population. Finally, an implementation on commercial mobile and cloud platforms is used to validate the performance of the model. Keywords-Distributed systems, mobile computing, security. I.
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 | 0.002 |
| 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 | 0.002 | 0.004 |
| Open science | 0.002 | 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".