Privacy-Preserving Batch-based Task Assignment in Spatial Crowdsourcing with Untrusted Server

TL;DR

This paper introduces a privacy-preserving batch task assignment method for spatial crowdsourcing that uses Geo-Indistinguishability and homomorphic encryption to maximize task assignments without privacy leaks.

Contribution

The paper proposes the k-Switch solution combining group-based assignment and homomorphic encryption for privacy-preserving batch task assignment in spatial crowdsourcing.

Findings

k-Switch improves task assignment success by 5.9X over baselines.

It outperforms online solutions by 1.74X in task assignment.

No privacy leaks are observed in the proposed method.

Abstract

In this paper, we study the privacy-preserving task assignment in spatial crowdsourcing, where the locations of both workers and tasks, prior to their release to the server, are perturbed with Geo-Indistinguishability (a differential privacy notion for location-based systems). Different from the previously studied online setting, where each task is assigned immediately upon arrival, we target the batch-based setting, where the server maximizes the number of successfully assigned tasks after a batch of tasks arrive. To achieve this goal, we propose the k-Switch solution, which first divides the workers into small groups based on the perturbed distance between workers/tasks, and then utilizes Homomorphic Encryption (HE) based secure computation to enhance the task assignment. Furthermore, we expedite HE-based computation by limiting the size of the small groups under k. Extensive experiments demonstrate that, in terms of the number of successfully assigned tasks, the k-Switch solution improves batch-based baselines by 5.9X and the existing online solution by 1.74X, with no privacy leak.