WiLoVe: A WiFi-coverage based Location Verification System in LBS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract With the proliferation of mobile devices, location-based services (LBS) have penetrated into our daily life in recent years. In an LBS system, the users check in at different venues to acquire rewards such as virtual points or real-world coupons/discounts, and easily share with their friends the recent activities. While these rewards benefit benign users a lot, they are incentives for malicious users to cheat on their locations. Recent researches have revealed that location cheating can be launched automatically on a large scale at ease. In current practice, the defense against location cheating relies on the server side solution, which has been proven to be insufficient. In this paper, we propose a novel verification system named WiLoVe. It maps the physical area of a venue to the local WiFi coverage and involves the venue owner as the verifier, hence utilizes the user's capability of one-hop communication with the verifier to verify the user's presence at the venue. In this work, we implement the verifier as an independent application, which can easily be installed on the venue owner's existing devices (laptops, cellphones, etc.). Hence, no additional hardware is needed. An adaptive algorithm is designed to defend against proxy attacks based on the check-in delay. Extensive experiments show that WiLoVe achieves low false rate, as well as consistent user experience, acceptable power consumption, and good applicability to different WiFi environments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it