Location Privacy Challenges in Spatial Crowdsourcing

Alharthi, Raed; Banihani, Abdelnasser; Alzahrani, Abdulrahman; Alshehri, Ali; Alshahrani, Hani; Fu, Huirong; Liu, Anyi; Zhu, Ye

doi:10.1109/eit.2018.8500311

Cited by 8 publications

(12 citation statements)

References 22 publications

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“…Location Privacy-preserving in pull mode has been studied in term of participatory sensing in [21], [22], [23], [24]. A recent survey provides an overview of location privacy attacks in the pull mode and push mode of tasking in [7]. The latest survey that focuses on this topic can be found in [6].…”

Section: Related Workmentioning

confidence: 99%

“…In their approach, crowd workers can cloak their location using either distributed or centralized mechanisms based on other crowd workers locations. Unfortunately, the adversaries can infer the crowd worker's locations if the crowd worker lactated in a sparsely populated area [7]. Another differential kanonymity-based has been proposed for location privacy [29].…”

Section: Related Workmentioning

confidence: 99%

“…The existing SC-servers require crowd workers to disclose their precise locations in order to efficaciously assign them the tasks and avoid long travel distance. A knowledge of location information of the crowd workers may be utilized to perform several location privacy attacks, which are demonstrated in [7], where adversaries can disclose the crowd workers' private information. In fact, many of the SC-servers may not be trusted and reveal crowd workers location information [8].…”

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confidence: 99%

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Protecting Location Privacy for Crowd Workers in Spatial Crowdsourcing Using a Novel Dummy-Based Mechanism

et al. 2020

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Spatial Crowdsourcing (SC) is a new valuable paradigm, relies on crowd workers to perform a set of spatial-temporal tasks at specific locations. It has garnered attention in collecting and processing social, environmental, and other spatio-temporal data by the contribution of individuals, communities and groups of workers in the physical world. The objective of SC is to outsource a set of spatio-temporal tasks to a set of workers, which requires the workers to be physically traveling to the tasks' locations in order to perform them, i.e., taking photos or collecting real time weather information at pre-specified location. Existing solutions require crowd workers to disclose their precise locations to untrustworthy service providers. Location updates and tracking in spatial crowdsourcing raise several privacy concerns in that malicious parties could snoop on crowd workers' whereabouts. Thus, the crowd workers' privacy could be compromised by disclosing their locations to untrusted and possibly malicious parties. This paper provides a novel framework called Dummies' Centroid (DCentroid), which aims at preserving location privacy for crowd workers in SC. The framework adapts an anonymous communication technique using a dummy based approach to generate dummy locations, i.e. decoy locations, and send their centroid points (pseudolocations) to service providers for processing. This paper theoretically analyzes the DCentroid framework and guarantees the crowd workers' privacy, while preserving the functionality of SC, such as the success rate of task assignments, worker travel distances, and system overhead. Practical experimentation on real-world datasets shows that the DCentroid framework protects the crowd workers' location privacy without affecting the various performance parameters of task assignment. INDEX TERMS DCentroid, location privacy, pseudolocation, spatial crowdsourcing. I. INTRODUCTION T HE term crowdsourcing was first coined by Jeff Howe in 2006 in his article titled "The Rise of Crowdsourcing" in [1]. Since then, it has been a widely used umbrella term and a hot topic in the field of computer science. As stated by Jeff Howe, crowdsourcing is simply clarified as, an open call to an undefined large group of people to take a job that was traditionally performed by a designated agent (usually an employee) [2]. Typically, plenty of people can easily participate in crowdsourcing since it happens online such as Amazon Mechanical Turk (AMT) [3]. Those people can complete any desired task posted by corporations or individuals based on their own knowledge, usually for a small amount of money. With the significant growth of crowdsourcing, the area of Spatial Crowdsourcing (SC) has recently been a popular research topic. SC is a platform for performing spatial-temporal tasks that requires crowd workers to physically travel to the locations of the tasks to perform them. Typically, the tasks submitted by a requester to a centralized spatial crowdsourcing server (SC-server) that act as a speculator between the request...

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Protecting Location Privacy for Crowd Workers in Spatial Crowdsourcing Using a Novel Dummy-Based Mechanism

et al. 2020

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“…There are many attacks that can happen on pull mode such as task sampling attack, location homogeneity attack, and map matching attack [6]. The first attack is the task sampling attack in which the attackers link the location of participants to location-based tasks to know the location of a specific worker.…”

Section: Location-based Privacy Threatsmentioning

confidence: 99%

“…With the growth of the MCS models, there have been other platforms extended from the MCS concept like Spatial Crowdsourcing (SC). The main characteristic of SC is that workers must be present in a specific location to accomplish the spatial tasks [6]. In this paper, we use both terms exchangeably.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Location Privacy-Preserving Mechanisms in Mobile Crowdsourcing

Bashanfar¹,

Alzahrani²,

Alutebei³

et al. 2019

IJACSA

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Mobile Crowdsourcing (MCS) surfaced as a new affluent method for data collection and processing as a result of the boom of sensor-rich mobile devices popularity. MCS still has room for improvement, particularly in protecting workers' private information such as location. Therefore, the installation of privacy-preserving mechanisms that insulate sensitive information and prevent attackers from obtaining information is a necessity. In this paper, we discuss location privacy threats and analyze some recently proposed mechanisms that targeted location privacy in mobile crowdsourcing. Finally, we compare and evaluate these mechanisms according to specific criteria that we define in this paper.

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