Differential privacy in health research: A scoping review
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
OBJECTIVE: Differential privacy is a relatively new method for data privacy that has seen growing use due its strong protections that rely on added noise. This study assesses the extent of its awareness, development, and usage in health research. MATERIALS AND METHODS: A scoping review was conducted by searching for ["differential privacy" AND "health"] in major health science databases, with additional articles obtained via expert consultation. Relevant articles were classified according to subject area and focus. RESULTS: A total of 54 articles met the inclusion criteria. Nine articles provided descriptive overviews, 31 focused on algorithm development, 9 presented novel data sharing systems, and 8 discussed appraisals of the privacy-utility tradeoff. The most common areas of health research where differential privacy has been discussed are genomics, neuroimaging studies, and health surveillance with personal devices. Algorithms were most commonly developed for the purposes of data release and predictive modeling. Studies on privacy-utility appraisals have considered economic cost-benefit analysis, low-utility situations, personal attitudes toward sharing health data, and mathematical interpretations of privacy risk. DISCUSSION: Differential privacy remains at an early stage of development for applications in health research, and accounts of real-world implementations are scant. There are few algorithms for explanatory modeling and statistical inference, particularly with correlated data. Furthermore, diminished accuracy in small datasets is problematic. Some encouraging work has been done on decision making with regard to epsilon. The dissemination of future case studies can inform successful appraisals of privacy and utility. CONCLUSIONS: More development, case studies, and evaluations are needed before differential privacy can see widespread use in health research.
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 | 0.016 | 0.280 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.030 | 0.043 |
| Research integrity | 0.000 | 0.004 |
| 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