Visible to the public Privacy-Preserving Online Task Assignment in Spatial Crowdsourcing with Untrusted Server

TitlePrivacy-Preserving Online Task Assignment in Spatial Crowdsourcing with Untrusted Server
Publication TypeConference Paper
Year of Publication2018
AuthorsTo, Hien, Shahabi, Cyrus, Xiong, Li
Conference Name2018 IEEE 34th International Conference on Data Engineering (ICDE)
Date Publishedapr
KeywordsAnalytical models, crowdsourcing, data privacy, geo indistinguishability, location based services, location privacy, Measurement, Metrics, Personnel, perturbed locations, privacy, privacy models and measurement, privacy-preserving online task assignment, probability, pubcrawl, Servers, spatial crowdsourcing, spatiotemporal tasks, system overhead, Task Analysis, task locations, worker locations, worker travel distance, worker-task pair reachability
AbstractWith spatial crowdsourcing (SC), requesters outsource their spatiotemporal tasks (tasks associated with location and time) to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. However, current solutions require the locations of the workers and/or the tasks to be disclosed to untrusted parties (SC server) for effective assignments of tasks to workers. In this paper we propose a framework for assigning tasks to workers in an online manner without compromising the location privacy of workers and tasks. We perturb the locations of both tasks and workers based on geo-indistinguishability and then devise techniques to quantify the probability of reachability between a task and a worker, given their perturbed locations. We investigate both analytical and empirical models for quantifying the worker-task pair reachability and propose task assignment strategies that strike a balance among various metrics such as the number of completed tasks, worker travel distance and system overhead. Extensive experiments on real-world datasets show that our proposed techniques result in minimal disclosure of task locations and no disclosure of worker locations without significantly sacrificing the total number of assigned tasks.
Citation Keyto_privacy-preserving_2018