PPADT: Privacy-Preserving Identity-Based Public Auditing With Efficient Data Transfer for Cloud-Based IoT Data

Abstract

Public auditing is a significant technique in cloud-based IoT systems, which enables the verifier to check the integrity of IoT data stored in the cloud. Nowadays, data become a core property for owners. Once the data of one owner are sold to another one, the ownership of these data has to be transferred. However, the existing public auditing schemes with data transfer require all the authenticators corresponding to the transferred data to be transformed to the new ones for integrity auditing. It incurs significant computation cost because of re-computing the new authenticators for all transferred data, especially when a vast quantity of data is being transferred. In addition, the data privacy and the identity privacy of the data owner cannot be protected for the verifier in such schemes. Thus, how to achieve efficient data transfer and privacy protection are key challenges in public auditing with data transfer for cloud-based IoT data. In this paper, we propose a privacy-preserving identity-based public auditing scheme with efficient data transfer for cloud-based IoT data (PPADT). In PPADT, all the authenticators corresponding to the transferred data blocks do not need to be transformed. We only need to transform an aggregated authenticator in the integrity auditing phase. It means that the computation cost of data transfer is independent of the number of transferred data blocks. Furthermore, the data owner’s identity privacy can be ensured with the assistance of the private key generator. The data privacy can also be guaranteed by employing the random masking technique.