Loxin — A solution to password-less universal login
Bibliographic record
Abstract
As the easiest and cheapest way of authenticating an end user, password based authentication methods have been consistently chosen by almost every new cloud service. Unfortunately, the explosive growth of cloud services and web applications has made it impossible for users to manage dozens of passwords for accessing different cloud services. The situation is even worse considering the potential application of massively parallel computing devices such as GPU and ASIC for efficient password cracking. Hence, from a usability viewpoint, passwords may have reached the end of their useful life. Motivated by a number of recent industry initiatives for online authentication, we present Loxin, an innovative solution for password-less universal login. Loxin aims to improve on passwords with respect to both usability and security. Loxin takes advantages of push message services for mobile devices and enables users to access multiple cloud services by using pre-owned identities, such as email addresses, together with few taps on their mobile devices. In particular, the Loxin server cannot generate users' login credentials, thereby eliminating the potential risk of server compromises. Loxin is resistant to the most common attacks on cloud services such as replay attacks and man-in-the-middle attacks. We also discuss possible extensions for protecting Loxin from vendor lock-in and single point of failure, in order to ensure Loxin to be an open and stable authentication system. The application of the proposed Loxin security framework to the recent MintChip Challenge demonstrates the power of Loxin for building a real-world password-less mobile payment solution.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".