The Novel Location Privacy-Preserving CKD for Mobile Crowdsourcing Systems

Chi, Zhongyang; Wang, Yingjie; Huang, Yan; Tong, Xiangrong

doi:10.1109/access.2017.2783322

Cited by 39 publications

(21 citation statements)

References 31 publications

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“…As the number of meteorological workflows and data offloaded to cloud centre increases rapidly [13,14], increasing the resource utilisation of active nodes in cloud centre is also being paid more and more attention [15,16], and it has become an important indicator to measure the performance of placement method [17,18]. In addition, with the improvement of the confidentiality of meteorological data, there are some privacy conflicts between meteorological data, so that these conflicting meteorological data should avoid being placed on the same or neighbouring storage nodes to ensure the security of these privacy data [19][20][21]. Therefore, while improving the resource utilisation of nodes, the placement of conflict data is also receiving increasing IET Cyber-Phys.…”

Section: Nomenclaturementioning

confidence: 99%

Data privacy‐based coordinated placement method of workflows and data

Huang

Xue

Hu³

et al. 2020

IET cyber-phys. syst.

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With the rapid development of data acquisition technology, many industries data already have the characteristics of big data and cloud technology has provided strong support for the storage and complex calculations of these massive data. The meteorological department established the cloud data centre based on the existing storage and computing resources and rearranged the historical data to reduce the historical data access time of applications. However, the placement of each workflow and input data also affects the average data access time, which in turn affects the computing efficiency of the cloud data centre. At the same time, because of the collaborative processing of multiple nodes, the resource utilisation of cloud data centre has also been paid more and more attention. In addition, with the increase of data security requirements, some privacy conflict data should avoid being placed on the same or neighbouring nodes. In response to this challenge, based on the fat-tree network topology, this study proposes a data privacy protection-based collaborative placement strategy of workflow and data to jointly optimise the average data access time, the average resource utilisation, and the data conflict degree. Finally, a large number of experimental evaluations and comparative analyses verify the efficiency of the proposed method. T m Total data access time of the task t m T avg AC average data access time for M tasks U i col resource utilisation of the ith compute node U j sto resource utilisation of the jth storage node U average resource utilisation of all nodes C data conflict degree for all conflicting data inp

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Section: Nomenclaturementioning

confidence: 99%

Data privacy‐based coordinated placement method of workflows and data

Huang

Xue

Hu³

et al. 2020

IET cyber-phys. syst.

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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]. Unlike private location-based queries, this study focuses on trust-aware task allocation in mobile crowdsourcing.…”

Section: Task Allocation In Location-based Mobile Crowdsourcingmentioning

confidence: 99%

“…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]. The trustworthiness of mobile workers must be considered in the mobile crowdsourcing setting [12].…”

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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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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“…They also proposed a k-ε-differential privacy protection to prevent user’s location information from leaking. Chi et al [31] proposed a location privacy protection mechanism CKD (k-anonymity and differential privacy-preserving). Hu et al [32] proposed an incentive mechanism based on multi-attribute reverse auction for mobile crowdsourcing.…”

Section: Related Workmentioning

confidence: 99%

Task Allocation Model Based on Worker Friend Relationship for Mobile Crowdsourcing

Zhao

Wang

et al. 2019

Sensors

Self Cite

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With the rapid development of mobile devices, mobile crowdsourcing has become an important research focus. According to the task allocation, scholars have proposed many methods. However, few works discuss combining social networks and mobile crowdsourcing. To maximize the utilities of mobile crowdsourcing system, this paper proposes a task allocation model considering the attributes of social networks for mobile crowdsourcing system. Starting from the homogeneity of human beings, the relationship between friends in social networks is applied to mobile crowdsourcing system. A task allocation algorithm based on the friend relationships is proposed. The GeoHash coding mechanism is adopted in the process of calculating the strength of worker relationship, which effectively protects the location privacy of workers. Utilizing synthetic dataset and the real-world Yelp dataset, the performance of the proposed task allocation model was evaluated. Through comparison experiments, the effectiveness and applicability of the proposed allocation mechanism were verified.

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The Novel Location Privacy-Preserving CKD for Mobile Crowdsourcing Systems

Cited by 39 publications

References 31 publications

Data privacy‐based coordinated placement method of workflows and data

Data privacy‐based coordinated placement method of workflows and data

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

Task Allocation Model Based on Worker Friend Relationship for Mobile Crowdsourcing

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