A framework for protecting worker location privacy in spatial crowdsourcing

Abstract

Spatial Crowdsourcing (SC) is a transformative platform that engages individuals, groups and communities in the act of collecting, analyzing, and disseminating environmental, social and other spatio-temporal information. The objective of SC is to outsource a set of spatio-temporal tasks to a set of workers, i.e., individuals with mobile devices that perform the tasks by physically traveling to specified locations of interest. However, current solutions require the workers, who in many cases are simply volunteering for a cause, to disclose their locations to untrustworthy entities. In this paper, we introduce a framework for protecting location privacy of workers participating in SC tasks. We argue that existing location privacy techniques are not sufficient for SC, and we propose a mechanism based on differential privacy and geocasting that achieves effective SC services while offering privacy guarantees to workers. We investigate analytical models and task assignment strategies that balance multiple crucial aspects of SC functionality, such as task completion rate, worker travel distance and system overhead. Extensive experimental results on real-world datasets show that the proposed technique protects workers' location privacy without incurring significant performance metrics penalties.

Top-k Team Recommendation and Its Variants in Spatial Crowdsourcing

“…Kazemi et al [8] further studies the reliability of crowd workers based on [6]. To et al [9] studies the location privacy protection problem for the workers. Kazemi et al [8] studies the route planning problem for a crowd worker and tries to maximize the number of completed tasks.…”

“…Most existing studies on spatial crowdsourcing mainly focus on the problems of task assignment [6][7][8][9][10][11][12], which are to assign tasks to suitable workers, and assume that tasks are all simple and trivial. However, in real applications, there are many complex spatial crowdsourced tasks, which often need to be collaboratively completed by a team of crowd workers with different skills.…”

Privacy Issues in Urban Computing using Mobile Crowdsensing

“…Many different privacy preserving frameworks have been proposed in the literature, which apply techniques like Data Perturbation [5], Cryptography [11], Differential privacy [12], k-anonymity [13], Tessellation [14], Data aggregation [15], Pseudonyms [16] and Access control mechanisms [17]. Table II does a comparison of few such papers on the basis of attacks and threats dealt with, using various privacy techniques.…”

Cost Minimization and Social Fairness for Spatial Crowdsourcing Tasks

“…Several variants of the problem of assigning spatial tasks to mobile workers have been been studied under various sets of hypothesis [11,7,12,5,10,16].…”

“…The authors propose approximate solutions to the problem of assigning a set of workers to a set of tasks while maximizing reliability and diversity. The authors of [10,16] propose a differential privacy model for protecting location privacy of workers participating in spatial crowdsourcing tasks, which is not the concern in our problem setting.…”