Privacy Protection-Oriented Mobile Crowdsensing Analysis Based on Game Theory

Ma, Rong; Xiong, Jinbo; Lin, Mingwei; Yao, Zhiqiang; Lin, Hui; Ye, Ayong

doi:10.1109/trustcom/bigdatase/icess.2017.342

Cited by 14 publications

(12 citation statements)

References 28 publications

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“…The paper [19] proposes the privacy protectionoriented MC (PPMC) scheme to continually provide the high-quality data in the MC process. First, the PPMC scheme gives a formal definition of the sensing user's contribution based on the accuracy in data analysis.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, the PPMC scheme can motivate more users to participate in sensing tasks and provides high-quality data over a long period of time. Finally, an efficient solution is given by the prisoner's dilemma between the service provider and the mediator [19].…”

Section: Related Workmentioning

confidence: 99%

“…Some earlier studies [17][18][19][20] have attracted considerable attention while introducing unique challenges in handling the MC control problems. In this paper, we demonstrate that our proposed scheme significantly outperforms these existing BidGuard [17], APMC [18], and PPMC [19] schemes.…”

Section: Related Workmentioning

confidence: 99%

“…Performance analysis: we evaluate the performance of the proposed scheme based on the simulation model. Numerical study demonstrates that the overall system performance of our proposed scheme can be significantly improved by comparing to the existing [17][18][19] schemes.…”

Section: Introductionmentioning

confidence: 95%

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A new differential privacy preserving crowdsensing scheme based on the Owen value

Kim

2019

J Wireless Com Network

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The Internet of Everything (IoE) paradigm makes the Internet more pervasive, interconnecting every devices of everyday life, and it is a promising solution for the development of 5G network services. Nowadays, Internetconnected devices are equipped with various built-in sensors. Therefore, the concept of mobile crowdsensing (MC) has been introduced to the IoE-driven situation where mobile devices gather data with the aim of performing a specific application. In this paper, we propose a new cooperative game model for the privacy-driven device collaboration in the MC system. The major goal of our approach is to incentivize the participating devices for effective data acquisitions while protecting each individual privacy based on each device's preference. According to the Owen value mechanism, the proposed scheme provides an effective payment solution for each MC participating device under privacy considered IoE environments. The main merit possessed by our MC control approach is to guide the cooperation of mobile devices in providing MC services. Performance evaluation reveals the superiority of our proposed scheme in terms of task success ratio, MC participating ratio, and payoff fairness. Finally, we provide the guidance on the future research direction of the MC system including other issues.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

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A new differential privacy preserving crowdsensing scheme based on the Owen value

Kim

2019

J Wireless Com Network

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“…The security of private information in mobile crowdsensing includes the privacy of the sensing users and the security of the service provider. The sensing users anticipate that their personal data privacy is preserved during the uploading to service provider [21,22]. Since the sensing users and the mediator may not be completely trusted, the uploaded false data may potentially cause security problems to the service provider, and therefore countermeasures to malicious users and malicious attacks should be considered.…”

Section: Privacy Protection Schemementioning

confidence: 99%

Achieving Incentive, Security, and Scalable Privacy Protection in Mobile Crowdsensing Services

Xiong

Chen

et al. 2018

Wireless Communications and Mobile Computing

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Mobile crowdsensing as a novel service schema of the Internet of Things (IoT) provides an innovative way to implement ubiquitous social sensing. How to establish an effective mechanism to improve the participation of sensing users and the authenticity of sensing data, protect the users' data privacy, and prevent malicious users from providing false data are among the urgent problems in mobile crowdsensing services in IoT. These issues raise a gargantuan challenge hindering the further development of mobile crowdsensing. In order to tackle the above issues, in this paper, we propose a reliable hybrid incentive mechanism for enhancing crowdsensing participations by encouraging and stimulating sensing users with both reputation and service returns in mobile crowdsensing tasks. Moreover, we propose a privacy preserving data aggregation scheme, where the mediator and/or sensing users may not be fully trusted. In this scheme, differential privacy mechanism is utilized through allowing different sensing users to add noise data, then employing homomorphic encryption for protecting the sensing data, and finally uploading ciphertext to the mediator, who is able to obtain the collection of ciphertext of the sensing data without actual decryption. Even in the case of partial sensing data leakage, differential privacy mechanism can still ensure the security of the sensing user's privacy. Finally, we introduce a novel secure multiparty auction mechanism based on the auction game theory and secure multiparty computation, which effectively solves the problem of prisoners' dilemma incurred in the sensing data transaction between the service provider and mediator. Security analysis and performance evaluation demonstrate that the proposed scheme is secure and efficient.

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A privacy‐preserving mechanism for social mobile crowdsensing using game theory

Esmaeilyfard

Esmaili

2022

Trans Emerging Tel Tech

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Smartphones and mobile networks have created a new paradigm called mobile crowdsensing for data gathering and processing about a large-scale phenomenon. This paradigm's development allows untrustworthy people to gather large amounts of information over time from the reported events. In this regard, mobile crowdsensing increases the possibility of violating users' privacy. Recently, social communication has added potential capabilities to mobile crowdsourcing environments. Based on these social communications, this article presents an analysis of user's interactions for privacy-preserving using a signaling game with a dynamic approach and incomplete information. First, the analysis identifies the pure-strategy profiles and mixed-strategy profiles and the user's best strategy for accepting access requests in a single time slot. This analysis is then extended into a dynamic multistage game. In this analysis, users' beliefs about the violation of their privacy are analyzed and updated at every stage. As a result, the best response from users will be determined. Following this analysis, a mechanism is presented for privacy-preserving in social mobile crowdsensing. The experimental results have also indicated the success of the proposed method in dealing with different types of user interaction.

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Privacy Protection-Oriented Mobile Crowdsensing Analysis Based on Game Theory

Cited by 14 publications

References 28 publications

A new differential privacy preserving crowdsensing scheme based on the Owen value

A new differential privacy preserving crowdsensing scheme based on the Owen value

Achieving Incentive, Security, and Scalable Privacy Protection in Mobile Crowdsensing Services

A privacy‐preserving mechanism for social mobile crowdsensing using game theory

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