6,133 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
Protecting location privacy in spatial crowdsourcing using encrypted data
© 2017, Copyright is with the authors. In spatial crowdsourcing, spatial tasks are outsourced to a set of workers in proximity of the task locations for efficient assignment. It usually requires workers to disclose their locations, which inevitably raises security concerns about the privacy of the workersâ locations. In this paper, we propose a secure SC framework based on encryption, which ensures that workersâ location information is never released to any party, yet the system can still assign tasks to workers situated in proximity of each taskâs location. We solve the challenge of assigning tasks based on encrypted data using homomorphic encryption. Moreover, to overcome the efficiency issue, we propose a novel secure indexing technique with a newly devised SKD-tree to index encrypted worker locations. Experiments on real-world data evaluate various aspects of the performance of the proposed SC platform
Geographic differential privacy for mobile crowd coverage maximization
For real-world mobile applications such as location-based advertising and spatial crowdsourcing, a key to success is targeting mobile users that can maximally cover certain locations in a future period. To find an optimal group of users, existing methods often require information about users' mobility history, which may cause privacy breaches. In this paper, we propose a method to maximize mobile crowd's future location coverage under a guaranteed location privacy protection scheme. In our approach, users only need to upload one of their frequently visited locations, and more importantly, the uploaded location is obfuscated using a geographic differential privacy policy. We propose both analytic and practical solutions to this problem. Experiments on real user mobility datasets show that our method significantly outperforms the state-of-the-art geographic differential privacy methods by achieving a higher coverage under the same level of privacy protection
Understanding the Valuation of Location Privacy: a Crowdsourcing-Based Approach
The exchange of private information for services or other benefits is a commonplace practice today in the advent of mobile technology. In the case of mobile services, the exchanged commodity is increasingly often spatial location of the user. To decide whether this transaction is beneficial, the user needs to evaluate the exchange value of this commodity. To assess the value users give to their location, and to understand its relationship with location sharing, we conducted a study on a mobile crowdsourcing platform (N=190). We find that users\u27 valuation of location privacy is dependent on the sharing scenario. For instance, when the location is to be shared with an untrusted advertiser, the users require a premium as compensation for their information. Additionally, benefit perception and trust are found to be connected with more frequent location sharing, while perceived risks and privacy concern are associated with sharing oneâs location less frequently
Context-Aware Hierarchical Online Learning for Performance Maximization in Mobile Crowdsourcing
In mobile crowdsourcing (MCS), mobile users accomplish outsourced human
intelligence tasks. MCS requires an appropriate task assignment strategy, since
different workers may have different performance in terms of acceptance rate
and quality. Task assignment is challenging, since a worker's performance (i)
may fluctuate, depending on both the worker's current personal context and the
task context, (ii) is not known a priori, but has to be learned over time.
Moreover, learning context-specific worker performance requires access to
context information, which may not be available at a central entity due to
communication overhead or privacy concerns. Additionally, evaluating worker
performance might require costly quality assessments. In this paper, we propose
a context-aware hierarchical online learning algorithm addressing the problem
of performance maximization in MCS. In our algorithm, a local controller (LC)
in the mobile device of a worker regularly observes the worker's context,
her/his decisions to accept or decline tasks and the quality in completing
tasks. Based on these observations, the LC regularly estimates the worker's
context-specific performance. The mobile crowdsourcing platform (MCSP) then
selects workers based on performance estimates received from the LCs. This
hierarchical approach enables the LCs to learn context-specific worker
performance and it enables the MCSP to select suitable workers. In addition,
our algorithm preserves worker context locally, and it keeps the number of
required quality assessments low. We prove that our algorithm converges to the
optimal task assignment strategy. Moreover, the algorithm outperforms simpler
task assignment strategies in experiments based on synthetic and real data.Comment: 18 pages, 10 figure
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