Real-time task assignment in hyperlocal spatial crowdsourcing under budget constraints

To, Hien; Fan, Liyue; Tran, Luan; Shahabi, Cyrus

doi:10.1109/percom.2016.7456507

Cited by 80 publications

(66 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kazemi and Sahabi [11] study the maximum task assignment (MTA) problem in spatial crowdsourcing, which aims at maximizing the overall number of assigned tasks and considers that a worker only accepts tasks within his/her spatial region and is only willing to perform up to a predetermined number of tasks. To et al [14] introduce a framework for crowdsourcing hyper-local information. A task can only be answered by workers who are already within a radius r from the task location at a time when the task is valid.…”

Section: Related Workmentioning

confidence: 99%

In-route task selection in crowdsourcing

Costa

Nascimento

2018

Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

View full text Add to dashboard Cite

One important problem in crowdsourcing is that of assigning tasks to workers. We consider a scenario where a worker is traveling on a preferred/typical path (e.g., from school to home) and there is a set of tasks available to be performed. Furthermore, we assume that: each task yields a positive reward, the worker has the skills necessary to perform all available tasks and he/she is willing to possibly deviate from his/her preferred path as long as he/she travels at most a total given distance/time. We call this problem the In-Route Task Selection (IRTS) problem and investigate it using the skyline paradigm in order to obtain the exact set of non-dominated solutions, i.e., good and diverse solutions yielding different combinations of smaller or larger rewards while traveling more or less. This is a practically relevant problem as it empowers the worker as he/she can decide, in real time, which tasks suit his/her needs and/or availability better. After showing that the IRTS problem is NP-hard, we propose an exact (but expensive) solution and a few others practical heuristic solutions. While the exact solution is suitable only for reasonably small IRTS instances, the heuristic solutions can produce solutions with good values of precision and recall for problems of realistic sizes within practical, in fact most often sub-second, query processing time.

show abstract

Section: Related Workmentioning

confidence: 99%

In-route task selection in crowdsourcing

Costa

Nascimento

2018

Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

View full text Add to dashboard Cite

show abstract

“…While crowdsourcing has largely been used by both research communities (e.g., databases) and industry (e.g., oDesk and Amazon Mechanical Turk), spatial crowdsourcing only recently received rising attention (e.g., [27], [30], [29], [28], [23], [18] and [17]). Location privacy has been studied first within the model of spatial k-anonymity [10], [22], where the location of a user is hidden among k other users.…”

Section: Related Workmentioning

confidence: 99%

Differentially Private Location Protection for Worker Datasets in Spatial Crowdsourcing

Ghinita

Fan

et al. 2016

IEEE Trans. on Mobile Comput.

Self Cite

View full text Add to dashboard Cite

Spatial Crowdsourcing (SC) is a transformative platform that engages individuals in collecting and analyzing environmental, social and other spatio-temporal information. SC outsources spatio-temporal tasks to a set of workers, i.e., individuals with mobile devices that perform the tasks by physically traveling to specified locations. However, current solutions require the workers to disclose their locations to untrusted parties. In this paper, we introduce a framework for protecting location privacy of workers participating in SC tasks. We propose a mechanism based on differential privacy and geocasting that achieves effective SC services while offering privacy guarantees to workers. We address scenarios with both static and dynamic (i.e., moving) datasets of workers. Experimental results on real-world data show that the proposed technique protects location privacy without incurring significant performance overhead.

show abstract

“…Location entropy has been extensively used in various areas of research, including multi-agent systems [25], wireless sensor networks [26], geosocial networks [4,3,17], personalized web search [14], image retrieval [29] and spatial crowdsourcing [12,23,20], etc. The study that most closely relates to ours focuses on privacy-preserving location-based services in which location entropy is used as the measure of privacy or the attacker's uncertainty [28,24].…”

Section: Related Workmentioning

confidence: 99%

Differentially private publication of location entropy

Nguyen

Shahabi

2016

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Self Cite

View full text Add to dashboard Cite

Location entropy (LE) is a popular metric for measuring the popularity of various locations (e.g., points-of-interest). Unlike other metrics computed from only the number of (unique) visits to a location, namely frequency, LE also captures the diversity of the users' visits, and is thus more accurate than other metrics. Current solutions for computing LE require full access to the past visits of users to locations, which poses privacy threats. This paper discusses, for the first time, the problem of perturbing location entropy for a set of locations according to differential privacy. The problem is challenging because removing a single user from the dataset will impact multiple records of the database; i.e., all the visits made by that user to various locations. Towards this end, we first derive non-trivial, tight bounds for both local and global sensitivity of LE, and show that to satisfy -differential privacy, a large amount of noise must be introduced, rendering the published results useless. Hence, we propose a thresholding technique to limit the number of users' visits, which significantly reduces the perturbation error but introduces an approximation error. To achieve better utility, we extend the technique by adopting two weaker notions of privacy: smooth sensitivity (slightly weaker) and crowd-blending (strictly weaker). Extensive experiments on synthetic and real-world datasets show that our proposed techniques preserve original data distribution without compromising location privacy.

show abstract

Real-time task assignment in hyperlocal spatial crowdsourcing under budget constraints

Cited by 80 publications

References 16 publications

In-route task selection in crowdsourcing

In-route task selection in crowdsourcing

Differentially Private Location Protection for Worker Datasets in Spatial Crowdsourcing

Differentially private publication of location entropy

Contact Info

Product

Resources

About