Privacy-preserving public auditing for educational multimedia data in cloud computing

Nowadays, as distance learning is being widly used, multimedia data becomes an effective way for delivering educational contents in online educational systems. To handle the educational multimedia data efficiently, many distance learning systems adopt a cloud storage service. Cloud computing and storage services provide a secure and reliable access to the outsourced educational multimedia contents for users. However, it brings challenging security issues in terms of data confidentiality and integrity. The straightforward way for the integrity check is to make the user download the entire data for verifying them. But, it is inefficient due to the large size of educational multimedia data in the cloud. Recently many integrity auditing protocols have been proposed, but most of them do not consider the data privacy for the cloud service provider. Additionally, the previous schemes suffer from dynamic management of outsourced data. In this paper, we propose a public auditing protocol for educational multimedia data outsourced in the cloud storage. By using random values and a homomorphic hash function, our proposed protocol ensures data privacy for the cloud and the third party auditor (TPA). Also, it is secure against lose attack and temper attack. Moreover, our protocol is able to support fully dynamic auditing. Security and performance analysis results show that the proposed scheme is secure while guaranteeing minimum extra computation costs.