PPCS: An Intelligent Privacy-Preserving Mobile-Edge Crowdsensing Strategy for Industrial IoT
Bibliographic record
Abstract
Mobile-edge crowdsensing is capable of providing a large amount of data via pervasive mobile terminals for Industrial Internet of Things (IIoT). However, the generated data often contain users' sensitive information, which suggests the significance of privacy preserving in data aggregation and analysis for IIoT. Privacy preserving in mobile-edge crowdsensing have conflicting objectives, i.e., the edge fusion center (FC) requires data of better quality for data fusion with higher accuracy whereas participatory users (PUs) desire better privacy preserving by larger noise injection. Therefore, how to select proper noises to achieve the tradeoff between accuracy and privacy is a challenging problem. In addition, FC is subject to data tempering due to the lack of data reliability validations and incentive mechanisms. To tackle these problems, we propose a novel privacy-preserving mobile-edge crowdsensing strategy (PPCS) for IIoT. Specifically, PPCS provides a Kullback-Leibler privacy-preserving data aggregation using a reputation-based incentive mechanism. On the other hand, PPCS offers hypothesis test-based data reliability validation and PU's reputation update, which collaborate to ease the impact of tampered data. Meanwhile, a reinforcement learning algorithm, the expected Sarsa, is applied to obtain the optimal test threshold. Theoretical analysis and experimental results show that PPCS is an energy-efficient strategy and the data provided by PPCS has a better aggregation accuracy than certain baseline strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".