Privacy-Preserving Proof-of-Location With Security Against Geo-Tampering
Bibliographic record
Abstract
A Proof-of-Location (POL) system is used to issue a proof-of-location token ($pol$) to a user who has been present at a location$\ell oc$, such that it can be later presented to a verifier to assure the presence of the user at$\ell oc$. Basic POL security requirements areunforgeabilityof$pol$, and itsnon-transferability(a$pol$issued to user$u_1$cannot be used by$u_2$). An additional important property of POL systems isuser privacyagainst the issuers and verifiers. We make two contributions. First, we formalize the POL security and privacy properties, and construct the first system providing provable security and privacy against the issuer and the verifier, both. Second, we introduce ageo-tampering attackthat completely breaks POL system security, by simply changing the location of a$pol$issuing node. The attack applies to portable infrastructure nodes that are not continually monitored. We propose an algorithm that is used by a$pol$issuer to provide a location integrity “proof”, that will be embedded in a$pol$to protect against this attack. The proof relies on a novel application of euclidean Distance Matrices. We implemented our POL on an off-the-shelf Android smartphone to show the practicality of the proposed algorithms.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.016 |
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".