Device-independent Tokens with Password Protection Against Token Stealing
Bibliographic record
Abstract
Single Sign-On (SSO) systems permit an identity provider (IdP) to issue a token to an authenticated user that can be presented to a third party, referred to as the relying party (RP), for authentication and access. The RP will verify the IdP’s signature on the token and if valid, accepts the user’s identity claim. SSO systems are widely used because they allow the user to authenticate themself to different service providers using a single password. Basic tokens are not bound to the user’s identity and if stolen, allow the adversary to impersonate the user. Token stealing can be prevented by embedding the user’s public key in the token. This, however, requires using the corresponding private key, in a challenge-response protocol, that must be stored in the device’s secure element, effectively binding the token to the device(s) that store the private key.In this paper we propose a novel approach to token protection that removes both above shortcomings by using a password pwdvthat can be different from the authentication password. The user embeds a function of pwdvin the token during the token issuing, and token presentation requires the proof of knowledge of pwdv. The tokens, called Proof-of-Password (PoPwd) possession, can be used with any RP without prior password registration. We give a construction and prove its security against a powerful adversary that fully controls the network and has access to token generation and verification oracles. We also provide a proofof-concept implementation of PoPwd, and compare it with two other commonly used token systems, one with and one without protection against token stealing, that we implement, showing feasibility and advantages of our system in practice.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".