A security framework for DICOM images in health information systems
Bibliographic record
Abstract
With the increasing use of information technology in the health care domain to- day, hospitals are interconnected to share medical data, thereby providing a distributed environment for storing and accessing medical data.As data is accessed from various locations in such distributed environment, security becomes a major concern because security lapses such as unauthorized access, eavesdropping, masquerading, intrusion, and data integrity violation could easily occur.Medical data is highly sensitive, so several provinces in Canada and various countries across the world have defined policies and have introduced laws to maintain the privacy and confidentiality of medical data.Without a proper security infrastructure, it would be highly difficult to maintain the privacy and confidentiality of medical data.The major challenge in health information systems is to perform authorization check (i.e., access control) locally, while providing access to medical data globally without violating the privacy of medical data.Providing access control scalable across hospitals is quite complex because of the following two reasons:(a) each hospital has its own policies (b) in a hospital external users from other hospitals become aliens.The main objective of this research is to provide a security framework for sharing Digital Imaging and Com- munications in Medicine (DICOM) images in radiology information systems (RIS).We designed a hybrid access control model by combining the properties of team-based ac- cess control and rule-based role delegation models.We use the trust relationship among hospitals to make the hybrid access control model scalable across hospitals.The secu- rity framework provides fine-grained access control, policy management, demographics filtering, and log maintenance constrained to PHIA (Personal Health Information Act lt of 1997) and the DICOM standard.Our emphasis is towards fine-grained access control and log maintenance.The security framework will maintain privacy and confidentiality of DICOM images without degrading the performance of RIS considerably.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".