Integrating encryption techniques for secure data storage in the cloud
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
Abstract Cloud computing has emerged as one of the most groundbreaking technologies to have redefined the bounds of conventional computing techniques. It has ushered in a paradigm shift and pushed the frontiers of how computing assets, inclusive of infrastructure resources, software, and applications can be used, adopted, and purchased. The economic benefits or rather the fundamental economic shift offered by cloud computing in reducing capital expenditure and converting it to operational expenditure has been a primary motivating factor for early adopters. However, despite its inherent advantages that include better access and control, there exist several reservations around cloud computing that have impeded its growth. The control, elasticity, and ease of use that cloud computing is associated with also engender many security issues. Security is considered to be the topmost hurdle out of the nine identified challenges of cloud computing as underlined by the study conducted by the International Data Corporation. It therefore follows that an exceedingly secure system is essential for the safeguarding of an organizational entity, its resources, and assets. In this article, it is our endeavor to offer insights into the implementation of a novel architecture that can deliver an enhanced degree of security for outsourcing information in a cloud computing environment while involving numerous independent cloud providers. The framework comprises of dual encryption and data fragmentation techniques that envision the secure distribution of information in a multicloud environment. The various concerns surrounding this area, specifically, the challenges of integrity, security, confidentiality, and authentication have been addressed. All simulations and scrutiny have been accomplished on an Oracle virtual machine Virtual‐Box and a Fog environment on an Ubuntu 16.04 platform. Extensive safety measures and performance analysis that take into account diverse parameters, especially execution time, integrity, throughput, entropy, transfer rate, and delay demonstrate that our projected proposal is vastly proficient and satisfies the security prerequisites of secure data sharing and can efficiently withstand security attacks.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.009 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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