Composable Anonymous Proof-of-Location With User-Controlled Offline Access
Bibliographic record
Abstract
A proof-of-location ($pol$) is a digital credential issued to a user after proving their location to an issuer. The user can use the$pol$at a later time to prove to a verifier that they have been present at a claimed location. A secure Proof-of-Location (POL) system requires that$pol\text{s}$be unforgeable and non-transferable to other users. POL systems can be used to provide fine-grained authentication and authorization and must ensure the privacy of the$pol$owner against the issuer and the verifier while allowing efficient presentation of$pol\text{s}$combined with other credentials when needed. Efficiency is in terms of communication overhead in user-verifier POL sessions, which has particular significance in high-volume$pol$verification scenarios. We first propose a POL system that (i) is provably secure in a simulation-based framework, allowing a$pol$to be securely used with other credentials, and (ii) provides anonymity against the issuer and the verifier. We then extend the system to allow$pol\text{s}$to be stored on a public distributed ledger system and selectively be presented to the verifiers by the user. This is the first POL system that satisfies the above properties. We implement POL algorithms on a mobile phone and present our experimental results showing the practicality of the system. Our proposed scheme is highly scalable compared to existing systems, reducing the user-verifier POL communication overhead by up to a factor of 94.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".