A Large-Scale Concurrent Data Anonymous Batch Verification Scheme for Mobile Healthcare Crowd Sensing

Recently, with the rapid development of big data, Internet of Things (IoT) brings more and more intelligent and convenient services to people's daily lives. Mobile healthcare crowd sensing (MHCS), as a typical application of IoT, is becoming an effective approach to provide various medical and healthcare services to individual or organizations. However, MHCS still have to face to different security challenges in practice. For example, how to quickly and effectively authenticate masses of bio-information uploaded by IoT terminals without revealing the owners' sensitive information. Therefore, we propose a large-scale concurrent data anonymous batch verification scheme for MHCS based on an improved certificateless aggregate signature. The proposed scheme can authenticate all sensing bio-information at once in a privacy preserving way. The individual data generated by different users can be verified in batch, while the actual identity of participants is hidden. Moreover, assuming the intractability of computational Diffie-Hellman problem, our scheme is proved to be secure. Finally, the performance evaluation shows that the proposed scheme is suitable for MHCS, due to its high efficiency.

I. Introduction

Internet of Things (IoT), as a promising paradigm, can change the interactive way between networks and the physical world [1]. Meanwhile, with the popularization and development of wireless sensors, a new perceptual architecture—mobile crowd sensing (MCS) [2], [3], has emerged. It provides an important technical support for the integration of the physical world with higher layer applications in IoT. As an important application branch of MCS, mobile healthcare crowd sensing (MHCS) provides more convenient medical and healthcare services for organizations or individual.