How Strong are Passwords Used to Protect Personal Health Information in Clinical Trials?
Bibliographic record
Abstract
BACKGROUND: Findings and statements about how securely personal health information is managed in clinical research are mixed. OBJECTIVE: The objective of our study was to evaluate the security of practices used to transfer and share sensitive files in clinical trials. METHODS: Two studies were performed. First, 15 password-protected files that were transmitted by email during regulated Canadian clinical trials were obtained. Commercial password recovery tools were used on these files to try to crack their passwords. Second, interviews with 20 study coordinators were conducted to understand file-sharing practices in clinical trials for files containing personal health information. RESULTS: We were able to crack the passwords for 93% of the files (14/15). Among these, 13 files contained thousands of records with sensitive health information on trial participants. The passwords tended to be relatively weak, using common names of locations, animals, car brands, and obvious numeric sequences. Patient information is commonly shared by email in the context of query resolution. Files containing personal health information are shared by email and, by posting them on shared drives with common passwords, to facilitate collaboration. CONCLUSION: If files containing sensitive patient information must be transferred by email, mechanisms to encrypt them and to ensure that password strength is high are necessary. More sophisticated collaboration tools are required to allow file sharing without password sharing. We provide recommendations to implement these practices.
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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 | 0.292 | 0.726 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".