An Information Security System for Image Encryption Applications: Architecture and Performance Evaluation
Bibliographic record
Abstract
Among many socio-industrial sectors of a technologically driven society, including but not limited to the military, education, and business, healthcare has been the most targeted institution. There are many underlying reasons, namely, legacy software and technologies used by healthcare providers, lack of systematic data governance, non-robust infrastructure, insufficient training for employees, individual sets of regulations and governance for each province in Canada, and insufficient control over data access by staff. Surprisingly, the personal information of patients (address, SIN number, phone number, etc.) and patients’ records are sometimes even not encrypted on the cloud. Most of the attacks happen on the cloud which can lead to catastrophic consequences (data usually is stored on public clouds without implementing any guards like zero trust). There are many algorithms developed after 1974 to provide security (confidentiality, authentication, integration, access control, etc.). However, these algorithms are not efficient solutions for healthcare data including images. There is an urgent need for a class of efficient and optimized algorithms that can be used by all healthcare centers as a standard to provide fast and secure encryption. In this thesis, a novel information security system is proposed to incorporate an innovative and emerging class of cryptographic algorithms. Unlike existing algorithms in the literature, these new algorithms exhibit unique properties, which make them particularly suitable for delivering a practical and efficient architecture for securing telemedicine. Accordingly, a comprehensive and strategic panoply of tests is developed and examined in this research to investigate the practical real-world performance of this class of algorithms, and to prove their engineering suitability as the heart of the proposed information security system. The results show that this class of algorithms is superior to publicly known cryptographic methods, notably being resistant against currently known classical and quantum attack schemes. Altogether, from an information security perspective, these findings reinforce the merits of the proposed system as a compelling competitor against state-of-the-art solutions for engineering an efficient and secure telemedicine architecture. Moreover, this research presents the design and implementation of a graphical user interface (GUI) tailored for the execution and assessment of emerging classes of cryptographic algorithms. Recognizing the growing need for accessible and user-friendly cryptographic tools, this work addresses the gap between complex algorithmic implementations and end-users.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".