Truthful incentive mechanism with location privacy-preserving for mobile crowdsourcing systems

Wang, Yingjie; Cai, Zhipeng; Tong, Xiangrong; Gao, Yang; Yin, Guisheng

doi:10.1016/j.comnet.2018.02.008

Cited by 124 publications

(51 citation statements)

References 14 publications

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“…Considering the private participating mobile devices [8], Tran and To et al proposed a real-time algorithm for spatial task allocation in server-assigned crowdsourcing [5]. This framework can be employed to protect the real locations of mobile workers and to maximize the crowdsourcing success rates [10,11].…”

Section: Task Allocation In Location-based Mobile Crowdsourcingmentioning

confidence: 99%

“…Moreover, the success of mobile crowdsourcing relies heavily on the quality of location-related workers [7]. The existing crowdsourcing systems are dependent on mainly mobile workers to allocate tasks to themselves when logging on to the systems [8], and many spatial tasks may not be allocated to suitable workers [9]. The execution quality of the crowdsourcing tasks suffers because the workers may be malicious participants [10][11][12][13].…”

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confidence: 99%

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A trust-aware task allocation method using deep q-learning for uncertain mobile crowdsourcing

Sun

Tan

2019

Hum. Cent. Comput. Inf. Sci.

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IntroductionWith the advancing technology of mobile devices with numerous built-in sensors, mobile crowdsourcing has recently emerged as a new collaboration paradigm in numerous intelligent mobile information systems [1]. The existing mobile crowdsourcing has applications in numerous domains including urban planning, traffic monitoring, ride sharing, environmental monitoring and intelligent disaster response [2]. Mobile crowdsourcing is a combination of spatial crowdsourcing and smart phone technology that employs mobile workers to perform certain tasks in a specific AbstractMobile crowdsourcing has emerged as a promising collaboration paradigm in which each spatial task requires a set of mobile workers in near vicinity to the target location. Considering the desired privacy of the participating mobile devices, trust is considered to be an important factor to enable effective collaboration in mobile crowdsourcing. The main impediment to the success of mobile crowdsourcing is the allocation of trustworthy mobile workers to nearby spatial tasks for collaboration. This process becomes substantially more challenging for large-scale online spatial task allocations in uncertain mobile crowdsourcing systems. The uncertainty can mislead the task allocation, resulting in performance degradation. Moreover, the large-scale nature of real-world crowdsourcing poses a considerable challenge to spatial task allocation in uncertain environments. To address the aforementioned challenges, first, an optimization problem of mobile crowdsourcing task allocation is formulated to maximize the trustworthiness of workers and minimize movement distance costs. Second, for the uncertain crowdsourcing scenario, a Markov decision process-based mobile crowdsourcing model (MCMDP) is formulated to illustrate the dynamic trust-aware task allocation problem. Third, to solve large-scale MCMDP problems in a stable manner, this study proposes an improved deep Q-learning-based trust-aware task allocation (ImprovedDQL-TTA) algorithm that combines trust-aware task allocation and deep Q-learning as an improvement over the uncertain mobile crowdsourcing systems. Finally, experimental results illustrate that the ImprovedDQL-TTA algorithm can stably converge in a number of training iterations. Compared with the reference algorithm, our proposed algorithm achieves effective solutions on the experimental data sets. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Section: Task Allocation In Location-based Mobile Crowdsourcingmentioning

confidence: 99%

mentioning

confidence: 99%

A trust-aware task allocation method using deep q-learning for uncertain mobile crowdsourcing

Sun

Tan

2019

Hum. Cent. Comput. Inf. Sci.

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“…Wang et al [113] proposed a two-stage auction algorithm taking both trust degree and privacy sensibility into consideration for mobile crowdsourcing systems, such as ridesharing practices. The k-anonymity scheme is integrated with -differential scheme to add Gaussian white noise to the actual locations of users.…”

Section: 2015mentioning

confidence: 99%

Emerging Privacy Issues and Solutions in Cyber-Enabled Sharing Services: From Multiple Perspectives

et al. 2019

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Fast development of sharing services has become a crucial part of the cyber-enabled world construction process, as sharing services reinvent how people exchange and obtain goods or services. However, privacy leakage or disclosure remains as a key concern during the sharing service development process. While significant efforts have been undertaken to address various privacy issues in recent years, there is a surprising lack of review for privacy concerns in the cyber-enabled sharing world. To bridge the gap, in this study, we survey and evaluate existing and emerging privacy issues relating to sharing services from various perspectives. Differing from existing similar works on surveying sharing practices in various fields, our work comprehensively covers six branches of sharing services in the cyberenabled world, and selects solutions mostly from the recent five to six years. We conclude the issues and solutions from three perspectives, namely, from users', platforms' and service providers' perspectives. Hot topics and less discussed (cold) topics are identified, which provides hints to researchers for their future studies.

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“…The improved twostage auction algorithm based on trust degree and privacy sensibility is proposed. The differential privacy based on Gauss white noise is applied to k-anonymity to prevent user location information leakage [9]. Zhang HT.…”

Section: Research Statusmentioning

confidence: 99%

“…For example, the location privacy protection development model and task allocation strategy in mobile cloud computing of ad hoc networks are proposed in [12]. A truthful incentive mechanism to protect location privacy in mobile crowdsourcing system is proposed in document [9]. And location service providers have a lot of user privacy information.…”

Section: Comparative Analysismentioning

confidence: 99%

The Research of Mobile Location Privacy Protection Access Control Method Based on Game Theory

Zheng

Zhang

Cui

et al. 2018

Wireless Communications and Mobile Computing

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In recent years, the Internet of things has developed rapidly. And the location-based service (LBS) is becoming more and more extensive. Service providers hold a large number of users’ information. In order to improve the quality of service, service providers increasingly use big data technology to provide more accurate services for users. At the same time, it aggravates the information disclosure of users’ privacy. From the perspective of the service provider, a mobile location privacy access control method based on game theory is proposed to solve the access control problem of mobile location privacy information. Firstly, the weight coefficient is set according to the location privacy influence factors, and then the access control threshold is calculated according to the privacy location leakage situation of the mobile location. Different visitor levels are set according to the threshold. In the process of access control, the prejudgement of the access behaviour is performed, and then the privacy information amount of the information requested for access is calculated according to the weights of the different information. Compare the result with the threshold and get the access control strategy. The strategy set is selected based on strategy matrix of game theory and thresholds are adjusted based on the calculation returns of strategy matrix. The effectiveness and practicability of the method are verified through the security analysis.

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Truthful incentive mechanism with location privacy-preserving for mobile crowdsourcing systems

Cited by 124 publications

References 14 publications

A trust-aware task allocation method using deep q-learning for uncertain mobile crowdsourcing

A trust-aware task allocation method using deep q-learning for uncertain mobile crowdsourcing

Emerging Privacy Issues and Solutions in Cyber-Enabled Sharing Services: From Multiple Perspectives

The Research of Mobile Location Privacy Protection Access Control Method Based on Game Theory

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