Privacy-Preserving Location-Based Advertising via Longitudinal Geo-Indistinguishability
Bibliographic record
Abstract
As location data have been increasingly adopted in location-based advertising (LBA), revealing locations to untrusted service providers has raised severe privacy concerns. Recent studies propose obfuscation mechanisms built upon geo-indistinguishability (geo-IND) to provide formal privacy guarantee. Unfortunately, due to the high degree of spatiotemporal regularity in human mobility pattern, the privacy cost will be unacceptably high in this situation, leading to accurate inference of user real locations. In this study, we identify this privacy risk in LBA scenarios under long-term and multi-platform assumption. We demonstrate an attacker can infer 75%∼90% of top-1 locations within a range of only 200 meters. To address it, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PrivLocAd</i> , a novel system which can provide longitudinal privacy guarantee. The novelty of PrivLocAd stems from a novel surrogate-based obfuscation, which generates multiple surrogate locations to improve the privacy-utility trade-off. In addition, two novel obfuscation mechanisms, the two-stage Gaussian and multi-level surrogate generation mechanism in charge of surrogate generation can achieve the longitudinal privacy guarantee in intra- and inter-platform condition respectively. Our experimental results demonstrate PrivLocAd is able to defend against the attack, which reduces the inference rate to less than 1% of user top-1 locations in the 200 meter range.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.016 | 0.002 |
| Research integrity | 0.000 | 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".