Bibliographic record
Abstract
Machine learning has widely been used in crowdsourcing systems to analyze the behavior of their mobile users. However, it naturally raises privacy concerns, as personal data needs to be collected and analyzed in the cloud, and results need to be sent back to the users to improve their local estimates. In this paper, we focus on the use of a specific type of learning algorithms, called maximum a posteriori (MAP) inference, in crowdsourcing systems, and use a crowdsourced localization system as an example. With MAP inference, the accuracy of each estimate of the user state may be improved by analyzing other users' estimates. Naturally, the privacy of the user state needs to be protected. Within the general framework of differential privacy, we show how private user states can be perturbed while preserving statistically accurate results. For the crowdsourcing system, we design a non-interactive mechanism for a group of users to perform inference without revealing their true states to any other party. The mechanism is implemented and verified in an indoor localization system. By comparing with the state-of-the-art, we have shown that our proposed privacy-preserving mechanism produces highly accurate results efficiently.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".