4.5 Article

Privacy-Preserving Public Auditing for Secure Cloud Storage

Journal

IEEE TRANSACTIONS ON COMPUTERS
Volume 62, Issue 2, Pages 362-375

Publisher

IEEE COMPUTER SOC
DOI: 10.1109/TC.2011.245

Keywords

Data storage; privacy preserving; public auditability; cloud computing; delegation; batch verification; zero knowledge

Funding

  1. US National Science Foundation (NSF) [CNS-1054317, CNS-1116939, CNS-1156318, CNS-1117111]
  2. Amazon
  3. Division Of Computer and Network Systems
  4. Direct For Computer & Info Scie & Enginr [1155988] Funding Source: National Science Foundation
  5. Division Of Computer and Network Systems
  6. Direct For Computer & Info Scie & Enginr [1156318] Funding Source: National Science Foundation

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Using cloud storage, users can remotely store their data and enjoy the on-demand high-quality applications and services from a shared pool of configurable computing resources, without the burden of local data storage and maintenance. However, the fact that users no longer have physical possession of the outsourced data makes the data integrity protection in cloud computing a formidable task, especially for users with constrained computing resources. Moreover, users should be able to just use the cloud storage as if it is local, without worrying about the need to verify its integrity. Thus, enabling public auditability for cloud storage is of critical importance so that users can resort to a third-party auditor (TPA) to check the integrity of outsourced data and be worry free. To securely introduce an effective TPA, the auditing process should bring in no new vulnerabilities toward user data privacy, and introduce no additional online burden to user. In this paper, we propose a secure cloud storage system supporting privacy-preserving public auditing. We further extend our result to enable the TPA to perform audits for multiple users simultaneously and efficiently. Extensive security and performance analysis show the proposed schemes are provably secure and highly efficient. Our preliminary experiment conducted on Amazon EC2 instance further demonstrates the fast performance of the design.

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