Optimal Task Recommendation for Mobile Crowdsourcing With Privacy Control

Abstract

Mobile crowdsourcing (MC) is a transformative paradigm that engages a crowd of mobile users (i.e., workers) in the act of collecting, analyzing, and disseminating information or sharing their resources. To ensure quality of service, MC platforms tend to recommend MC tasks to workers based on their context information extracted from their interactions and smartphone sensors. This raises privacy concerns hard to address due to the constrained resources on mobile devices. In this paper, we identify fundamental tradeoffs among three metrics-utility, privacy, and efficiency-in an MC system and propose a flexible optimization framework that can be adjusted to any desired tradeoff point with joint efforts of MC platform and workers. Since the underlying optimization problems are NP-hard, we present efficient approximation algorithms to solve them. Since worker statistics are needed when tuning the optimization models, we use an efficient aggregation approach to collecting worker feedbacks while providing differential privacy guarantees. Both numerical evaluations and performance analysis are conducted to demonstrate the effectiveness and efficiency of the proposed framework.