A Mail Client Plugin for Privacy-Preserving Spam Filter Evaluation.
Bibliographic record
Abstract
We describe a plugin extension to the Thunderbird Mail Client to support standardized evaluation of multiple spam filters on private mail streams. Researchers need not view or handle the subject users ’ messages and subject users need not be familiar with spam filter evaluation methodology. All that is required of the user is to install the plugin as a standard extension and to run it on his or her mailbox. The plugin evaluates a spam filter, assuming the user’s existing classification to be accurate, and sends summary results only to the researcher, after allowing the user to verify exactly what is sent. This plugin addresses an outstanding challenge in spam filter evaluation: that of using a broad base of realistic data while satisfying personal and legislative privacy requirements. Previous efforts have used public data which may not be representative, captured data which may be insufficiently private, and obfuscation techniques which compromise the integrity of the data and may also be insufficiently private. We show preliminary results using the tool to evaluate some filters previously evaluated at TREC. 1
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 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".