Achieving Private and Fair Truth Discovery in Crowdsourcing Systems

Wang, Zhenya; Cheng, Xiang; Su, Sen; Wang, Longhan

doi:10.1155/2022/9281729

Cited by 7 publications

(3 citation statements)

References 80 publications

(118 reference statements)

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“…Ignition delay time can be also described by the kinetic mechanism developed by Wang and coworkers [57,68]. This derives from the mechanism previously developed by Davis et al [57], which is based on the work of Mueller et al [56].…”

Section: Aq4mentioning

confidence: 96%

Syngas Production, Storage, Compression and Use in Gas Turbines

Yang

Zhou

et al. 2020

Biofuels and Biorefineries

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This chapters analyses syngas production through pyrolysis and gasification, its compression and its use in gas turbines. Syngas compression can be performed during or after thermal treatment processes. Important points are discussed related to syngas ignition, syngas explosion limit at high temperatures and high pressures and syngas combustion kinetics. Kinetic aspects influence ignition and final emissions which are obtained at the completion of the combustion process. The chapter is organised into four subsections, dealing with (1) innovative syngas production plants, (2) syngas compressors and compression process, (3) syngas ignition in both heterogeneous and homogeneous systems and (4) syngas combustion kinetics and experimental methods. Particular attention is given to ignition regions that affect the kinetics, namely systems that operate at temperatures higher than 1000 K can have strong ignition, whereas those operating at lower temperatures have weak ignition.

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Section: Aq4mentioning

confidence: 96%

Syngas Production, Storage, Compression and Use in Gas Turbines

Yang

Zhou

et al. 2020

Biofuels and Biorefineries

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“…However, it is very prone to adversarial attacks and does not ensure the quality of crowdsourced answers. There are several methods for inferring the truth of tasks and for performing aggregation of submissions, such as [9,[15][16][17][18]. They are classified based on the underlying techniques as direct computation [15], probability-based methods [19], optimization methods [20], and neural network-based (NN) methods [21].…”

Section: Truth Inference and Quality Control In Crowdsourcingmentioning

confidence: 99%

Classifying Workers for Mitigating Adversarial Attacks in Crowdsourcing

First,

Second

2023

Preprint

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Crowdsourcing is adopted as a fast and cost-effective system for human computation and acquiring data for training models in machine learning. Although crowdsourcing has broad applicability, it still has the following challenges. Normally, workers have access to crowdsourcing platforms with simple authentication mechanisms. As a result, malicious workers may get into the system and submit unreliable answers rendering the platform fraudulent. Moreover, when workers perform tasks, their lack of expertise and the difficulty of tasks affects the accuracy. Truth inference benefits the data quality and worker reliability. A truth inference algorithm estimates workers' trustworthiness and correctness of the answers from their responses. Besides, it helps in filtering out low-quality answers. However, due to adversarial attacks by malicious workers, ground truth inference algorithms perform poorly. This research considers how to defend against adversarial attacks on crowdsourcing platforms and improve the truth inference process. The proposed method estimates the trust and reliability scores of the workers and classifies them as normal and malicious workers. Based on this classification , tasks are assigned to the workers. Moreover, these predicted scores are used for inferring the correct answers, thereby improving the ground truth inference. Experiments consistently show that the proposed truth inference method is tolerant to adversarial attacks with competent accuracy.

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“…Wang et al [103] proposed a CS system which can execute a private and fair truth discovery (PFTD). The system is based on two noncollusion cloud servers and Paillier 7 Wireless Communications and Mobile Computing cryptosytem.…”

Section: Reputation System and Privacy-preserving Truthmentioning

confidence: 99%

Blockchain-Based Crowdsourcing Makes Training Dataset of Machine Learning No Longer Be in Short Supply

Wei

et al. 2022

Wireless Communications and Mobile Computing

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Recently, machine learning has become popular in various fields like healthcare, smart transportation, network, and big data. However, the labelled training dataset, which is one of the most core of machine learning, cannot meet the requirements of quantity, quality, and diversity due to the limitation of data sources. Crowdsourcing systems based on mobile computing seem to address the bottlenecks faced by machine learning due to their unique advantages; i.e., crowdsourcing can make professional and nonprofessional participate in the collection and annotation process, which can greatly improve the quantity of the training dataset. Additionally, distributed blockchain technology can be embedded into crowdsourcing systems to make it transparent, secure, traceable, and decentralized. Moreover, truth discovery algorithm can improve the accuracy of annotation. Reasonable incentive mechanism will attract many workers to provide plenty of dataset. In this paper, we review studies applying mobile crowdsourcing to training dataset collection and annotation. In addition, after reviewing researches on blockchain or incentive mechanism, we propose a new possible combination of machine learning and crowdsourcing systems.

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Achieving Private and Fair Truth Discovery in Crowdsourcing Systems

Cited by 7 publications

References 80 publications

Syngas Production, Storage, Compression and Use in Gas Turbines

Syngas Production, Storage, Compression and Use in Gas Turbines

Classifying Workers for Mitigating Adversarial Attacks in Crowdsourcing

Blockchain-Based Crowdsourcing Makes Training Dataset of Machine Learning No Longer Be in Short Supply

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