Quantum Computing and Cloud Security: Future-Proofing Healthcare Data Protection
Bibliographic record
Abstract
Cloud computing has emerged as the prevailing development in healthcare systems across the global market. These systems are creating and processing vast amounts of patient data that require a certain level of security. However, due to the introduction of quantum computation, the future of cryptographic techniques on which Cloud security relies is in danger. The following paper seeks to explore the compatibility of quantum computing and cloud security and regard to the protection of health data. It also includes a comprehensive analysis of the current risks, a discussion of already existing quantum-vulnerable points, and a strategy for creating a quantum-safe strategy for safe patient data storage in healthcare. The study under consideration also employs quantum cryptography and cloud structures to identify threats and create appropriate defence mechanisms. Some models explored and analyzed include Quantum Key Distribution (QKD), Post-Quantum Cryptography (PQC) and hybrid cryptosystems. A simulated hospital database has brought about the fragility of some of these algorithms, a research work dubbed as quantum resilience, in order to explain how it is possible to integrate these two concepts without removing the aspects of the cloud that make it appealing to many people, including scalability and accessibility. This indicates that there has been a major enhancement in standing against quantum attacks, specifically showing the way towards effective, sustainable and protected healthcare information systems.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".