Public Auditing with Privacy Protection in a Multi-User Model of Cloud-Assisted Body Sensor Networks

Abstract

Wireless Body Sensor Networks (WBSNs) are gaining importance in the era of the Internet of Things (IoT). The modern medical system is a particular area where the WBSN techniques are being increasingly adopted for various fundamental operations. Despite such increasing deployments of WBSNs, issues such as the infancy in the size, capabilities and limited data processing capacities of the sensor devices restrain their adoption in resource-demanding applications. Though providing computing and storage supplements from cloud servers can potentially enrich the capabilities of the WBSNs devices, data security is one of the prevailing issues that affects the reliability of cloud-assisted services. Sensitive applications such as modern medical systems demand assurance of the privacy of the users' medical records stored in distant cloud servers. Since it is economically impossible to set up private cloud servers for every client, auditing data security managed in the remote servers has necessarily become an integral requirement of WBSNs' applications relying on public cloud servers. To this end, this paper proposes a novel certificateless public auditing scheme with integrated privacy protection. The multi-user model in our scheme supports groups of users to store and share data, thus exhibiting the potential for WBSNs' deployments within community environments. Furthermore, our scheme enriches user experiences by offering public verifiability, forward security mechanisms and revocation of illegal group members. Experimental evaluations demonstrate the security effectiveness of our proposed scheme under the Random Oracle Model (ROM) by outperforming existing cloud-assisted WBSN models.

Keywords: cloud computing; privacy preserving; public auditing; wireless body sensor networks.

Figure 1

The system model of cloud-assisted WBSNs.

Figure 2

The network model of our proposed public auditing scheme. KGC, Key Generation Centre.

Figure 3

The flowchart of the attacking games in our security proof.

Figure 4

The time cost of the algorithm ProofGen, TagGen, Encryption and Decryption with regard to the numbers of data blocks in seconds.

Figure 5

The time cost of the algorithms PartialPrivateKeyExtract and JoinGroup with regard to the requesting number of users in seconds.

Figure 6

The time cost of the ProofVerify algorithm on the auditor sider with regard to the number of blocks in seconds.