Preserving Location Privacy for Outsourced Most-Frequent Item Query in Mobile Crowdsensing
Bibliographic record
Abstract
The emergence of mobile crowdsensing (MCS) has provided us with unprecedented opportunities for both sensing coverage and data transmission. However, in many MCS applications, the MCS workers are usually required to report the location information of the assigned tasks, which inevitably reveals the workers' location information, even trajectories, and severely impedes the popularization of the MCS system. It is believed that the query on the most-frequent location, e.g., querying the most congested location over a period in a city, is one of the most popular statistics queries in the MCS system, but it may disclose workers' location information. To address the issue, in this article, we propose a location privacy-preserving scheme for outsourced most-frequent item query in the MCS system, where two noncollusive semi-trusted cloud servers cooperatively handle the most-frequent item query. Specifically, by employing our pseudonymization mechanism, transposition cipher, ciphertext packing technique, and order-preserving merge function, our proposed scheme can efficiently answer the most-frequent item query while ensuring the privacy of both workers' personal information and query results. Detailed security analysis shows that our proposed scheme is privacy-preserving. In addition, extensive experiments are conducted, and the results show that our proposed scheme outperforms alternative schemes in terms of computational costs and communication overhead.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".