Geo-MOEA: A Multi-Objective Evolutionary Algorithm with Geo-obfuscation for Mobile Crowdsourcing Workers

TL;DR

Geo-MOEA introduces an adaptive geo-obfuscation framework combined with multi-objective evolutionary algorithms to enhance location privacy in spatial crowdsourcing, balancing privacy protection with service quality.

Contribution

It proposes a novel adaptive geo-indistinguishability based obfuscation method integrated with MOEA to optimize privacy and service trade-offs in large-scale mobile crowdsourcing.

Findings

Achieves up to 20% reduction in service quality loss

Guarantees differential and geo-distortion privacy

Effectively balances privacy and service availability

Abstract

The rapid development of mobile Internet and sharing economy brings the prosperity of Spatial Crowdsourcing (SC). SC applications assign various tasks according to reported location information of task's requesters and outsourced workers (such as DiDi, MeiTuan and Uber). However, SC-servers are often untrustworthy and the exposure of users' locations raises privacy concerns. In this paper, we design a framework called Geo-MOEA (Multi-Objective Evolutionary Algorithm with Geo-obfuscation) to protect location privacy of workers involved on SC platform in mobile networks environment. We propose an adaptive regionalized obfuscation approach with inference error bounds based on geo-indistinguishability (a strong notion of differential privacy), which is suitable for the context of large-scale location data and task allocations. This enables each worker to report a pseudo-location that is adaptively generated with a personalized inference error threshold. Moreover, as a popular computational intelligence method, MOEA is introduced to optimize the trade-off between SC service availability and privacy protection while ensuring theoretically the most general condition on protection location sets for larger search space. Finally, the experimental results on two public datasets show that our Geo-MOEA approach achieves up to 20% reduction in service quality loss while guaranteeing differential and geo-distortion location privacy.