Localization of credential information to address increasingly inevitable data breaches
Bibliographic record
Abstract
Large-scale data breaches exposing sensitive personal information are becoming commonplace. For numerous reasons, conventional personal (identification) information leaks from databases that store online and/or on-site user transaction data. Collected ID numbers and supporting personal information enable malicious parties to commit large-scale identity fraud. Gates and Slonim (NSPW 2003) proposed the owner-controlled information paradigm to address privacy violations of personal information where users are expected to maintain all their information using a personal device. Rubin and Wright (FC 2001), Molloy et al. (FC 2007), and others explored the use of one-time numbers to address credit card fraud (mostly for online use). However, several other types of ID number are at least as sensitive as credit card numbers. Our fundamental assumption is that collected personal information will eventually be breached. To combat identity fraud under this new environmental attack paradigm, we introduce a more general approach involving localized or customized ID numbers for both card-present and card-not-present transactions. We also explore four variants of the general idea to spark more discussion and further research in this area.
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 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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".