Dynamic Private Task Assignment under Differential Privacy

TL;DR

This paper introduces dynamic task assignment methods under differential privacy that enhance utility and efficiency in spatial crowdsourcing, allowing workers to improve their privacy-utility trade-off dynamically.

Contribution

It proposes two novel approaches, PUCE and PGT, that outperform existing methods in utility and speed for privacy-preserving task assignment.

Findings

PUCE achieves higher utility than previous methods.

PGT is 50-63% faster than PDCE.

PGT improves utility by 16% on average.

Abstract

Data collection is indispensable for spatial crowdsourcing services, such as resource allocation, policymaking, and scientific explorations. However, privacy issues make it challenging for users to share their information unless receiving sufficient compensation. Differential Privacy (DP) is a promising mechanism to release helpful information while protecting individuals' privacy. However, most DP mechanisms only consider a fixed compensation for each user's privacy loss. In this paper, we design a task assignment scheme that allows workers to dynamically improve their utility with dynamic distance privacy leakage. Specifically, we propose two solutions to improve the total utility of task assignment results, namely Private Utility Conflict-Elimination (PUCE) approach and Private Game Theory (PGT) approach, respectively. We prove that PUCE achieves higher utility than the state-of-the-art works. We demonstrate the efficiency and effectiveness of our PUCE and PGT approaches on both real and synthetic data sets compared with the recent distance-based approach, Private Distance Conflict-Elimination (PDCE). PUCE is always better than PDCE slightly. PGT is 50% to 63% faster than PDCE and can improve 16% utility on average when worker range is large enough.