Efficient task assignment in spatial crowdsourcing with worker and task privacy protection

Liu, An; Wang, Weiqi; Shang, Shuo; Li, Qing; Zhang, Xiangliang

doi:10.1007/s10707-017-0305-2

Cited by 86 publications

(28 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

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

Sun

Tan

2019

Hum. Cent. Comput. Inf. Sci.

View full text Add to dashboard Cite

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.

show abstract

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.

View full text Add to dashboard Cite

show abstract

“…In this paper, we consider a typical adversary model, that is, the semi-honest model [13], that has been widely accepted in a variety of privacy-preserving problem domains [21,18,20]. Specifically, all parties in this model are assumed to be semi-honest, that is, they follow the recommendation protocol exactly as specified, but may try to learn as much as possible about other parties' private input from what they see during the protocol's execution.…”

Section: Adversary Model and Security Definitionmentioning

confidence: 99%

Protecting multi-party privacy in location-aware social point-of-interest recommendation

Wang¹,

Liu²,

Li³

et al. 2018

World Wide Web

Self Cite

View full text Add to dashboard Cite

Q, et al. (2018) Protecting multi-party privacy in location-aware social point-ofinterest recommendation. World Wide Web. Available: http:// dx.Abstract Point-of-interest (POI) recommendation has attracted much interest recently because of its significant business potential. Data used in POI recommendation (e.g., user-location check-in matrix) are much more sparse than that used in traditional item (e.g., book and movie) recommendation, which leads to more serious cold start problem. Social POI recommendation has proved to be an effective solution, but most existing works assume that recommenders have access to all required data. This is very rare in practice because these data are generally owned by different entities who are not willing to share their data with others due to privacy and legal concerns. In this paper, we first propose PLAS, a protocol which enables effective POI recommendation without disclosing the sensitive data of every party getting involved in the recommendation. We formally show PLAS is secure in the semi-honest adversary model. To improve its performance. We then adopt the technique of cloaking area by which expensive distance computation over encrypted data is replaced by cheap operation over plaintext. In addition, we utilize the sparsity of check-ins to selectively publish data, thus reducing encryption cost and avoiding unnecessary computation over ciphertext. Experiments on two real datasets show that our protocol is feasible and can scale to large POI recommendation problems in practice.

show abstract

“…Recently there have also been some advances in privacy-preserving task assignment in SC applications [11][12][13][14] . In [11][12], workers' locations are collected and perturbed by a trusted party which injects calibrated noises into raw data according to differential privacy (DP) [15] .…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the update operation is very time-consuming especially when there are a large number of workers, which makes it unsuitable for large-scale real-time SC applications. The protocol proposed in [14] also takes into account worker velocity during task assignment, thus the result is more effective in practice. However, it still suffers from computation time issue and cannot scale to large SC applications.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Task Assignment in Spatial Crowdsourcing

Liu

et al. 2017

J. Comput. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

With the progress of mobile devices and wireless networks, spatial crowdsourcing (SC) is emerging as a promising approach for problem solving. In SC, spatial tasks are assigned to and performed by a set of human workers. To enable effective task assignment, however, both workers and task requesters are required to disclose their locations to untrusted SC systems. In this paper, we study the problem of assigning workers to tasks in a way that location privacy for both workers and task requesters is preserved. We first combine the Paillier cryptosystem with Yao's garbled circuits to construct a secure protocol that assigns the nearest worker to a task. Considering that this protocol cannot scale to a large number of workers, we then make use of Geohash, a hierarchical spatial index to design a more efficient protocol that can securely find approximate nearest workers. We theoretically show that these two protocols are secure against semi-honest adversaries. Through extensive experiments on two real-world datasets, we demonstrate the efficiency and effectiveness of our protocols.

show abstract

Efficient task assignment in spatial crowdsourcing with worker and task privacy protection

Cited by 86 publications

References 41 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

Protecting multi-party privacy in location-aware social point-of-interest recommendation

Privacy-Preserving Task Assignment in Spatial Crowdsourcing

Contact Info

Product

Resources

About