Evaluating Website Data Leaks through Spam Collection on Honeypots
Bibliographic record
Abstract
Nowadays, people rely heavily on online services in their daily lives such as communication, education, shopping, and entertainment.While online services offer convenience in daily living, users often receive a large number of spams as a result.While previous studies have linked spam receipt primarily to user behavior, this research proposes that spam can serve as a forensic indicator of data leaks by websites.To test our hypothesis, we conducted an experiment to deploy 148 honeypots across 370 websites spanning 12 communities.We monitored and audited the spams received by our honeypots for 47 weeks and analyzed their nature, pattern and origin.The results reveal that some legitimate websites leak user data despite having privacy policy statements.The findings also highlight that some websites automatically enroll users in newsletters or mailing lists without asking consent during the sign-up.This issue arises from conflating privacy policies with spam subscription and third party share agreements.To address these issues we suggest that regulators enforce websites to separate subscription agreement from privacy policy statements, and direct consent for third party share be requested at sign up.Also, websites should evaluate third party chain to ensure user data protection.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; 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".