Locally Differentially Private Truth Discovery Over Data Streams
Bibliographic record
Abstract
Data inconsistency often arises from multiple observed sensory data due to varying participant reliability for crowdsensing systems. Truth discovery, which includesWeight EstimationandTruth Aggregation, for estimating participant reliability weights and aggregating uploaded values from inconsistent observations respectively, has emerged as an effective solution to address this issue. While local differential privacy (LDP) provides strong privacy guarantees by allowing participants to perturb their data locally before submission, existing LDP-based studies are either designed for static scenarios or compromise on privacy and accuracy trade-off for data streams, satisfying only weaker versions of LDP or mere differential privacy. To effectively and efficiently obtain truths over streams under rigorous LDP, we proposeNANOwhich is locally differeNtially privAte truth discovery via updatiNg time stamp determinatiOn. The main idea lies in its integration of Laplacian noise for privacy protection and inherent Gaussian noise representing natural data variability for effective weight and truth estimations, coupled with the adaptive determination of updating time stamps. InNANO, to obtain theWeight EstimationandTruth Aggregationunder LDP, we design a mixed noise-aware truth discovery methodMixTDby modeling the mixed noise. To capture the dynamic nature of weight and truth evolutions, we develop a changing-aware updating time stamp determination methodCUDto selectively re-conduct truth discovery at specific time stamps. We also introduce a dynamic privacy budget management strategy, which accumulates unused budgets from skipped updates for critical timestamps. In this way,Weight EstimationandTruth Aggregationare limited to critical time stamps, which significantly reduces the privacy budget segmentation and computational costs. We demonstrate thatNANOprovides rigorous LDP guarantees while achieving bounded utility and computational complexity. Extensive experimental results over four real-world datasets and three synthetic datasets showcase thatNANOoutperforms the state-of-the-arts by at least 20% improvement with negligible extra efficiency loss.
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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 | 0.017 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".