PPTA: A location privacy-preserving and flexible task assignment service for spatial crowdsourcing

With the rapid growth of sensor-rich mobile devices, spatial crowdsourcing (SC) has emerged as a new crowdsourcing paradigm harnessing the crowd to perform location-dependent tasks. To appropriately select workers that are near the tasks, SC systems need to perform location-based task assignment, which requires collecting worker locations and task locations. Such practice, however, may easily compromise the location privacy of workers. In light of this, in this paper, we design, implement, and evaluate PPTA, a new system framework for location privacy-preserving task assignment in SC with strong security guarantees. PPTA takes advantage of only lightweight cryptography (such as additive secret sharing, function secret sharing, and secure shuffle), and provides a suite of tailored secure components required by practical location-based task assignment processes. Specifically, aiming for practical usability, PPTA is designed to flexibly support two realistic task assignment settings: (i) the online setting where tasks arrive and get processed at the SC platform one by one, and (ii) the batch-based setting where tasks arrive and get processed in a batch. Extensive experiments over a real-world dataset demonstrate that while providing strong security guarantees, PPTA supports task assignment with efficacy comparable to plaintext baselines and with promising performance.

Automated summary

With the increasing popularity of sensor-rich mobile devices, spatial crowdsourcing has become a new crowdsourcing paradigm that leverages the crowd to perform location-dependent tasks. However, location privacy of workers can be compromised in the process of location-based task assignment. To address this issue, this paper proposes PPTA, a system framework that ensures location privacy-preserving task assignment in spatial crowdsourcing with strong security guarantees. PPTA utilizes lightweight cryptography and provides tailored secure components for practical location-based task assignment processes. Experimental results on a real-world dataset demonstrate that PPTA achieves strong security while maintaining comparable efficiency to plaintext baselines.