Location Privacy-Aware Task Bidding and Assignment for Mobile Crowd-Sensing

Abstract

Mobile Crowd-Sensing (MCS) is a dominant sensing paradigm for Internet of Things (IoT), with a lot of potentials as it allows data collection through the user's sensor embedded mobile devices. The participation of people in IoT not only brings greater flexibility and sensing ability but also increases the risk of privacy breaches to the participants. Primarily, a worker's location data is vulnerable to information leaks as the task assignment in MCS is location-based. Most existing mechanisms that preserve worker's location in MCS are designed under the assumption that the platform is trusted, which may be not valid in real-world applications. Besides, the existing studies focus either on task selection problem for workers or task assignment problem for the platform. Therefore, this paper investigates both task bidding and assignment while preserving location privacy. We propose two task selection strategies: Minimize Total Cost (MTC) and Minimize Average Cost (MAC). Each worker submits a cost that is obfuscated using differential privacy to the platform. We propose probability cost-efficient worker selection mechanism (PCE-WSM) to determine winners and probability individual-rationality critical payment mechanism (PIR-CPM) to determine payments to winners. We prove that PIR-CPM is truthful and can achieve probability-individual rationality by theoretical analysis. To evaluate our proposed strategies, we conduct extensive experiments on both synthetic and real-world datasets, and the experimental results validate that PCE-WSM can achieve enough privacy preservation without incurring a high payment.