The inadvertent disclosure of personal health information through peer-to-peer file sharing programs
Bibliographic record
Abstract
OBJECTIVE: There has been a consistent concern about the inadvertent disclosure of personal information through peer-to-peer file sharing applications, such as Limewire and Morpheus. Examples of personal health and financial information being exposed have been published. We wanted to estimate the extent to which personal health information (PHI) is being disclosed in this way, and compare that to the extent of disclosure of personal financial information (PFI). DESIGN: After careful review and approval of our protocol by our institutional research ethics board, files were downloaded from peer-to-peer file sharing networks and manually analyzed for the presence of PHI and PFI. The geographic region of the IP addresses was determined, and classified as either USA or Canada. MEASUREMENT: We estimated the proportion of files that contain personal health and financial information for each region. We also estimated the proportion of search terms that return files with personal health and financial information. We ascertained and discuss the ethical issues related to this study. RESULTS: Approximately 0.4% of Canadian IP addresses had PHI, as did 0.5% of US IP addresses. There was more disclosure of financial information, at 1.7% of Canadian IP addresses and 4.7% of US IP addresses. An analysis of search terms used in these file sharing networks showed that a small percentage of the terms would return PHI and PFI files (ie, there are people successfully searching for PFI and PHI on the peer-to-peer file sharing networks). CONCLUSION: There is a real risk of inadvertent disclosure of PHI through peer-to-peer file sharing networks, although the risk is not as large as for PFI. Anyone keeping PHI on their computers should avoid installing file sharing applications on their computers, or if they have to use such tools, actively manage the risks of inadvertent disclosure of their, their family's, their clients', or patients' PHI.
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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 | 0.015 | 0.018 |
| 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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".