A privacy-preserving task recommendation framework for mobile crowdsourcing

Yanmin Gong, Yuanxiong Guo, Yuguang Fang

Abstract: Mobile crowdsourcing enables mobile workers to complete a broad range of crowdsourcing tasks anywhere at any time. However, recommending suitable crowdsourcing tasks to mobile workers requires sensitive information such as location and activity, which raises serious privacy concerns. In this paper, we formulate the task recommendation process as an optimization problem which balances privacy, utility, and efficiency. We show that this optimization problem is NP-hard, and present a greedy solution which approximates the optimal solution within a factor of 1 - 1/e. We also design an efficient aggregation protocol to compute statistics of mobile workers required in the optimization problem while providing strong privacy guarantee. Both numerical evaluations and performance analysis are carried out to show the effectiveness and efficiency of the proposed framework. To the best of our knowledge, our work is the first to consider privacy issues in task recommendation for mobile crowdsourcing.