Leveraging personal devices for stronger password authentication from untrusted computers
Bibliographic record
Abstract
Internet authentication for popular end-user transactions, such as online banking and e-commerce, continues to be dominated by passwords entered through end-user PCs. Most users continue to prefer (typically untrusted) PCs over smaller personal devices for actual transactions, due to usability features related to keyboard and screen size. However, most such transactions and their underlying protocols are vulnerable to attacks including keylogging, phishing and pharming. We propose Mobile Password Authentication (MP-Auth) to counter such attacks, which cryptographically separates a user's long-term secret input from the client PC, and offers transaction integrity. The PC continues to be used for most of the interaction but has access only to temporary secrets, while the user's long-term secret is input through an independent personal device, e.g., a cellphone which makes it available to the PC only after encryption under the intended far-end recipient's public key. MP-Auth expects users to input passwords only to a personal device, and be vigilant while confirming transactions from the device. To facilitate a comparison to MP-Auth, we also provide a comprehensive survey of web authentication techniques that use an additional factor of authentication; this survey may be of independent interest.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".