Privacy Preserving File Auditing Schemes for Cloud Storage using Verifiable Random Function

Abstract

Users leverage the cloud storage to store data by uploading files to the cloud storage. They can use it to share files to work on collaborative projects. However, during administrative operations by cloud storage service provider (CSSP), there may be some inadvertent corruption of files during data migration and backup. Due to heavy demands, the cloud service provider may not update the desired files immediately when requested by the owner. As a result, a user of the file may receive an obsolete file. To ensure integrity and freshness of files, third party auditing (TPA) services should be supported by CSSP while maintaining the confidentiality of user files and preserving privacy of users. In this paper, three privacy preserving auditing schemes for files stored in cloud has been proposed. Verifiable random function, merkle hash tree and ciphertext-policy attribute-based encryption has been used to achieve the desired goals.