BM-PDA: Blockchain Based Multifunctional Private-Preserving Data Aggregation for e-Health Systems
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
Secure aggregation of medical data enables detailed data analysis and informed medical decision-making in e-health systems, optimizing data resources utilization and enhancing service quality and decision accuracy. However, the collection of large volumes of medical data poses a significant risk of privacy leakage. Most existing privacy-preserving data aggregation schemes focus on additive aggregation of single or multi-dimensional data, which greatly limits their applicability. This article introduces a blockchain-based multifunctional data aggregation (BM-PDA) scheme for e-health systems. First, BM-PDA supports overall aggregation queries of data samples and can compute the maximum and minimum values within these samples. Second, it enables selective data aggregation queries based on various user attributes. Furthermore, analysis shows that integrating these two algorithms protects both user’s private data and attribute data. Performance evaluations indicate that the computational and communication costs are acceptable, demonstrating the scheme’s practical applicability.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it