A Sybil-Resistant Truth Discovery Framework for Mobile Crowdsensing

Lin, Jian; Yang, Dejun; Wu, Kun; Tang, Jian; Xue, Guoliang

doi:10.1109/icdcs.2019.00091

Cited by 12 publications

(3 citation statements)

References 26 publications

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“…The solution of data analysis usually focuses on the data analysis phase by using data processing methods such as Bayesian [ 18 ], machine learning clustering algorithm [ 19 ], and truth inference [ 20 , 21 ], in order to find the low quality of service. Lin et al [ 20 ] proposed a Sybil-resistant truth inference framework for MCS, which included three account grouping methods in pair with a truth inference algorithm to defend against the Sybil attacks. Liu et al [ 19 ] proposed a probabilistic model to jointly infer multi-dimensional trust of workers, multi-domain properties of questions, and true labels of questions.…”

Section: Related Workmentioning

confidence: 99%

“…With the strategies S2–S3, spam workers may acquire a “good” reputation and then continuously pose threats to crowdsourcing platforms. According to previous research, it can be divided into four categories to improve the service quality: (1) Verification-based Defense Model [ 15 , 16 , 17 ]; (2) Data Analysis Solution [ 18 , 19 , 20 , 21 ]; (3) Workers’ Properties Matching Mechanism [ 22 , 23 , 24 , 25 ]; and (4) Trust-based Model [ 4 , 26 , 27 , 28 ].Verification-based Defense Models adopt the method of re-checking the submitted answers to discover spam workers so that these models require a lot of resources. Data Analysis Solutions filter out valuable services by analyzing data submitted by workers.…”

Section: Introductionmentioning

confidence: 99%

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TruthTrust: Truth Inference-Based Trust Management Mechanism on a Crowdsourcing Platform

Zhou

Jin

et al. 2021

Sensors

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On a crowdsourcing platform, in order to cheat for rewards or sabotage the crowdsourcing processes, spam workers may submit numerous erroneous answers to the tasks published by requesters. This type of behavior extremely reduces the completion rate of tasks and the enthusiasm of honest users, which may lead a crowdsourcing platform to a failure. Defending against malicious attacks is an important issue in crowdsourcing, which has been extensively addressed by existing methods, e.g., verification-based defense mechanisms, data analysis solutions, trust-based defense models, and workers’ properties matching mechanisms. However, verification-based defense mechanisms will consume a lot of resources, and data analysis solutions cannot motivate workers to provide high-quality services. Trust-based defense models and workers’ properties matching mechanisms cannot guarantee the authenticity of information when collusion requesters publish shadow tasks to help malicious workers get more participation opportunities. To defend such collusion attacks in crowdsourcing platforms, we propose a new defense model named TruthTrust. Firstly, we define a complete life cycle system that from users’ interaction to workers’ recommendation, and separately define the trust value of each worker and the credence of each requester. Secondly, in order to ensure the authenticity of the information, we establish a trust model based on the CRH framework. The calculated truth value and weight are used to define the global properties of workers and requesters. Moreover, we propose a reverse mechanism to improve the resistance under attacks. Finally, extensive experiments demonstrate that TruthTrust significantly outperforms the state-of-the-art approaches in terms of effective task completion rate.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TruthTrust: Truth Inference-Based Trust Management Mechanism on a Crowdsourcing Platform

Zhou

Jin

et al. 2021

Sensors

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show abstract

“…Huang et al [10] researched potential combinations of traditional Automated Passenger Counters (APC) and a novel source capable of collecting detailed mobile demand data but did not include a reputation management module to prevent malicious data uploads. However, these mechanisms face several challenges in traditional crowdsourcing systems: 1) platform security lacks robust guarantees and may be susceptible to attacks [11]; 2) there exists a potential for large-scale privacy breaches [12]; and 3) incentive mechanisms relying on reputation scores may encounter issues as reputation updates hinge on task demander evaluations, occasionally lacking a dedicated reputation update module.…”

mentioning

confidence: 99%

Blockchain-Assisted Unsupervised Learning Method for Crowdsourcing Reputation Management

Wang,

Chen

2024

CMES

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Crowdsourcing holds broad applications in information acquisition and dissemination, yet encounters challenges pertaining to data quality assessment and user reputation management. Reputation mechanisms stand as crucial solutions for appraising and updating participant reputation scores, thereby elevating the quality and dependability of crowdsourced data. However, these mechanisms face several challenges in traditional crowdsourcing systems: 1) platform security lacks robust guarantees and may be susceptible to attacks; 2) there exists a potential for large-scale privacy breaches; and 3) incentive mechanisms relying on reputation scores may encounter issues as reputation updates hinge on task demander evaluations, occasionally lacking a dedicated reputation update module. This paper introduces a reputation update scheme tailored for crowdsourcing, with a focus on proficiently overseeing participant reputations and alleviating the impact of malicious activities on the sensing system. Here, the reputation update scheme is determined by an Empirical Cumulative distribution-based Outlier Detection method (ECOD). Our scheme embraces a blockchain-based crowdsourcing framework utilizing a homomorphic encryption method to ensure data transparency and tamper-resistance. Computation of user reputation scores relies on their behavioral history, actively discouraging undesirable conduct. Additionally, we introduce a dynamic weight incentive mechanism that mirrors alterations in participant reputation, enabling the system to allocate incentives based on user behavior and reputation. Our scheme undergoes evaluation on 11 datasets, revealing substantial enhancements in data credibility for crowdsourcing systems and a reduction in the influence of malicious behavior. This research not only presents a practical solution for crowdsourcing reputation management but also offers valuable insights for future research and applications, holding promise for fostering more reliable and high-quality data collection in crowdsourcing across diverse domains.

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Enhancing Mobile Crowdsensing Security: A Proof of Stake-Based Publisher Selection Algorithm to Combat Sybil Attacks in Blockchain-Assisted MCS Systems

Agrawal,

Bhatia,

Tiwari

2024

Lecture Notes on Data Engineering and Communications Technologies

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A Sybil-Resistant Truth Discovery Framework for Mobile Crowdsensing

Cited by 12 publications

References 26 publications

TruthTrust: Truth Inference-Based Trust Management Mechanism on a Crowdsourcing Platform

TruthTrust: Truth Inference-Based Trust Management Mechanism on a Crowdsourcing Platform

Blockchain-Assisted Unsupervised Learning Method for Crowdsourcing Reputation Management

Enhancing Mobile Crowdsensing Security: A Proof of Stake-Based Publisher Selection Algorithm to Combat Sybil Attacks in Blockchain-Assisted MCS Systems

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