Online multi-task allocation has become an essential research topic in Mobile Crowdsensing (MCS). Most existing studies merely focus on minimizing the total distance that workers need to travel, but ignore considering the total task rewards, which could lead to a reduction in the willingness of workers to complete tasks. In this paper, to incentivize workers to participate in tasks and protect their privacy, we propose a Location and Reward Privacy-Preserving based Secure Task Allocation(LRPP-STA) scheme. First, we design a secure distance computation method to obtain the distance from the workers to the tasks under location privacy preserving. Second, considering fixed reward for the task, we propose a Fixed Rewarding Secure Task Allocation(FR-STA) scheme, where a secure utility calculation method is proposed to calculate the encrypted utility of the worker upon completing tasks under rewards privacy preserving, along with the path planning for workers to maximize the total utility of the system through an Extended Maximum-Utility Flow model(EMUF). Third, considering the situation of dynamic task reward adjusted by requesters based on the supply and demand relationship as well as the urgency of the task, we propose a Dynamic Rewarding Secure Task Allocation(DR-STA) scheme to optimize the task allocation for workers while improving requesters satisfaction. Finally, we theoretically analyze the security of location and reward privacy-preserving scheme, and conduct extensive experiments with real-world datasets to verify that the secure task allocation scheme is effective in improving the total utility of workers compared to other baseline online tasking schemes.

Mobile Crowd Sensing (MCS) has become a prevalent large-scale and low-cost data collection paradigm by employing workers, and the location and bid privacy of both task and workers should not be leaked to the third party to prevent the adversary from attacking. Existing privacy preserving worker recruitment schemes have taken the location and quality into consideration, but ignore the bid privacy. To tackle this issue, a two-stage Location and Bid Privacy Preserving based Quality-aware Worker Recruitment (LBPP-QWR) scheme is proposed in this paper. In the first stage, to select those workers who satisfy the specified location and bid range of the task in the encrypted state, we propose a hybrid encryption scheme of matrix encryption and asymmetric encryption technique in the MCS platform. For the second stage, after obtaining the preliminary worker set via the platform, we propose a Knapsack Worker Selection (KWS) algorithm to recruit those high-quality and low bid workers under the budget constraint in the Data Requester (DR). Considering that there are quality-unknown workers, we further propose an improved.-KWS algorithm based on.-greedy algorithm by combining the exploration and exploitation mechanism to learn the quality of worker. Extensive experiments conducted on real-world datasets demonstrate that our proposed scheme can improve the average total quality by 17.96%-83.34%, and the cost efficiency by 27.99%-67.90% for the DR compared with other benchmark methods.