Deloc: Delegation-Based Privacy-Preserving Mechanism for Location-Based Services
Bibliographic record
Abstract
Location-based services (LBSs) are everywhere; we found them in several fields such as healthcare, entertainment, transportation, and many other daily activities. Besides, along with their presence in almost every daily task, their usefulness cannot be ignored. Moreover, with smartphone ownership growth, getting one's location became easier, and the privacy-related issues became almost inescapable. Hence, more severe solutions are strongly required to handle privacy issues and to keep LBS utility. We present in this paper Deloc, a novel location privacy protection mechanism founded on the idea of preserving location privacy without altering geographical coordinates. We introduce a new set of concepts and definitions that allow us to meet high privacy guarantees and data accuracy by delegating one's request to other LBS users. Similarly, we investigate the privacy issues related to LBSs along with state-of-art protection approaches and techniques. We evaluate Deloc on both synthetic and real-world data in a finely simulated environment with tunable parameters, and demonstrate its efficiency and usefulness in today's LBS applications, along with its higher privacy guarantees in respect to differential privacy standards.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.070 | 0.019 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".