PLP: Protecting Location Privacy Against Correlation Analyze Attack in Crowdsensing
Bibliographic record
Abstract
Crowdsensing applications require individuals to share local and personal sensing data with others to produce valuable knowledge and services. Meanwhile, it has raised concerns especially for location privacy. Users may wish to prevent privacy leak and publish as many non-sensitive contexts as possible. Simply suppressing sensitive contexts is vulnerable to the adversaries exploiting spatio-temporal correlations in the user's behavior. In this work, we present PLP, a crowdsensing scheme which preserves privacy while it maximizes the amount of data collection by filtering a user's context stream. PLP leverages a conditional random field to model the spatio-temporal correlations among the contexts, and proposes a speed-up algorithm to learn the weaknesses in the correlations. Even if the adversaries are strong enough to know the filtering system and the weaknesses, PLP can still provably preserve privacy, with little computational cost for online operations. PLP is evaluated and validated over two real-world smartphone context traces of 34 users. The experimental results show that PLP efficiently protects privacy without sacrificing much utility.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".