Privacy-Preserving Personal Health Record (P3HR)
Bibliographic record
Abstract
In contrast to the Electronic Medical Record (EMR) and Electronic Health Record (EHR) systems that are created to maintain and manage patient data by health professionals and organizations, Personal Health Record (PHR) systems are operated and managed by patients. Therefore, it necessitates increased attention to the importance of security and privacy challenges, as patients are most often unfamiliar with the potential security threats that can result from release of their health data. On the other hand, the use of PHR systems is increasingly becoming an important part of the healthcare system by sharing patient information among their circle of care. To have a system with a more favorable interface and a high level of security, it is crucial to provide a mobile application for PHR that fulfills six important features: (1) ease the usage for various patient demographics and their delegates, (2) security, (3) quickly transfer patient data to their health professionals, (4) give the ability of access revocation to the patient, (5) provide ease of interaction between patients and their circle of care, and (6) inform patients about any instances of access to their data by their circle of care. In this work, we propose an implementation of a Privacy-Preserving PHR system (P3HR) for Android devices to fulfill the above six characteristics, using a Ciphertext Policy Attribute Based Encryption to enhance security and privacy of the system, as well as providing access revocation in a hierarchical scheme of the health professionals and organizations involved. Using this application, patients can securely store their health data, share the records, and receive feedback and recommendations from their circle of care.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".