Messin ' with Texas Deriving Mother's Maiden Names Using Public Records
Bibliographic record
Abstract
We have developed techniques to automatically infer mother's maiden names from public records. We demonstrate our techniques using publicly available records from the state of Texas, and reduce the entropy of a mother's maiden name from an average of close to 13 bits down to below 6.9 bits for more than a quarter of the people targeted, and down to a zero entropy (i.e., certainty of their mothers maiden name) for a large number of targeted individuals. This poses a signi cant risk not only to individuals whose mothers maiden name can easily be guessed, but highlights the vulnerability of the system as such, given the traditional reliance of authentication by mother maiden names for nancial services. While our techniques and approach are novel, it is important to note that these techniques { once understood { do not require any insider information or particular skills to implement. This emphasizes the need to move away from mothers maiden names as an authenticator. Using the techniques described, during testing we were able to deduce the mother's maiden name for approximately 4,105,111 Texans. 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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".