Location-Proof System based on Secure Multi-Party Computations.
Bibliographic record
Abstract
Location-based services are quite popular. Their variety and their numerous users show it clearly. However, these applications rely on the persons’ honesty to use their real location. If they are motivated to lie about their position, they can do so. A location-proof system allows a prover to obtain proofs from nearby witnesses, for being at a given location at a given time. Such a proof can be used to convince a verifier later on. Many solutions have been designed in the last decade, but none protects perfectly the privacy of their participants. Indeed, provers and witnesses may want to keep their identity and location private. In this paper, a solution is presented in which a malicious adversary, acting as a prover, cannot cheat on his position. It relies on multi-party computations and group-signature schemes to protect the private information of both the prover and the witnesses against any semi-honest participant. Additionally, this paper gives a new secure multi-party maximum computation protocol requiring \(\mathcal {O}(n \log (n))\) computations and communications, which greatly improves the previously known solutions having \(\mathcal {O}(n^2)\) complexities. Although it is designed for our location-proof system, it can be applied to any scenario in which a small information leakage is acceptable.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".