3,249 research outputs found
Location Privacy in Spatial Crowdsourcing
Spatial crowdsourcing (SC) is a new platform that engages individuals in
collecting and analyzing environmental, social and other spatiotemporal
information. With SC, requesters outsource their spatiotemporal tasks to a set
of workers, who will perform the tasks by physically traveling to the tasks'
locations. This chapter identifies privacy threats toward both workers and
requesters during the two main phases of spatial crowdsourcing, tasking and
reporting. Tasking is the process of identifying which tasks should be assigned
to which workers. This process is handled by a spatial crowdsourcing server
(SC-server). The latter phase is reporting, in which workers travel to the
tasks' locations, complete the tasks and upload their reports to the SC-server.
The challenge is to enable effective and efficient tasking as well as reporting
in SC without disclosing the actual locations of workers (at least until they
agree to perform a task) and the tasks themselves (at least to workers who are
not assigned to those tasks). This chapter aims to provide an overview of the
state-of-the-art in protecting users' location privacy in spatial
crowdsourcing. We provide a comparative study of a diverse set of solutions in
terms of task publishing modes (push vs. pull), problem focuses (tasking and
reporting), threats (server, requester and worker), and underlying technical
approaches (from pseudonymity, cloaking, and perturbation to exchange-based and
encryption-based techniques). The strengths and drawbacks of the techniques are
highlighted, leading to a discussion of open problems and future work
On efficient and scalable time-continuous spatial crowdsourcing
The proliferation of advanced mobile terminals opened up a new crowdsourcing
avenue, spatial crowdsourcing, to utilize the crowd potential to perform
real-world tasks. In this work, we study a new type of spatial crowdsourcing,
called time-continuous spatial crowdsourcing (TCSC in short). It supports broad
applications for long-term continuous spatial data acquisition, ranging from
environmental monitoring to traffic surveillance in citizen science and
crowdsourcing projects. However, due to limited budgets and limited
availability of workers in practice, the data collected is often incomplete,
incurring data deficiency problem. To tackle that, in this work, we first
propose an entropy-based quality metric, which captures the joint effects of
incompletion in data acquisition and the imprecision in data interpolation.
Based on that, we investigate quality-aware task assignment methods for both
single- and multi-task scenarios. We show the NP-hardness of the single-task
case, and design polynomial-time algorithms with guaranteed approximation
ratios. We study novel indexing and pruning techniques for further enhancing
the performance in practice. Then, we extend the solution to multi-task
scenarios and devise a parallel framework for speeding up the process of
optimization. We conduct extensive experiments on both real and synthetic
datasets to show the effectiveness of our proposals
Crowdsourcing a Word-Emotion Association Lexicon
Even though considerable attention has been given to the polarity of words
(positive and negative) and the creation of large polarity lexicons, research
in emotion analysis has had to rely on limited and small emotion lexicons. In
this paper we show how the combined strength and wisdom of the crowds can be
used to generate a large, high-quality, word-emotion and word-polarity
association lexicon quickly and inexpensively. We enumerate the challenges in
emotion annotation in a crowdsourcing scenario and propose solutions to address
them. Most notably, in addition to questions about emotions associated with
terms, we show how the inclusion of a word choice question can discourage
malicious data entry, help identify instances where the annotator may not be
familiar with the target term (allowing us to reject such annotations), and
help obtain annotations at sense level (rather than at word level). We
conducted experiments on how to formulate the emotion-annotation questions, and
show that asking if a term is associated with an emotion leads to markedly
higher inter-annotator agreement than that obtained by asking if a term evokes
an emotion
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