Title

Attribute-Based Cloud Data Integrity Auditing for Secure Outsourced Storage

RIS ID

116887

Publication Details

Yu, Y., Li, Y., Yang, B., Susilo, W., Yang, G. & Bai, J. (2017). Attribute-Based Cloud Data Integrity Auditing for Secure Outsourced Storage. IEEE Transactions on Emerging Topics in Computing, Online first 1-13.

Abstract

IEEE Outsourced storage such as cloud storage can significantly reduce the burden of data management of data owners. Despite of a long list of merits of cloud storage, it triggers many security risks at the same time. Data integrity, one of the most burning challenges in secure cloud storage, is a fundamental and pivotal element in outsourcing services.Outsourced data auditing protocols enable a verifier to efficiently check the integrity of the outsourced files without downloading the entire file from the cloud, which can dramatically reduce the communication overhead between the cloud server and the verifier. Existing protocols are mostly based on public key infrastructure or an exact identity, which lacks flexibility of key management. In this paper, we seek to address the complex key management challenge in cloud data integrity checking by introducing attribute-based cloud data auditing, where users can upload files to cloud through some customized attribute set and specify some designated auditor set to check the integrity of the outsourced data. We formalize the system model and the security model for this new primitive, and describe a concrete construction of attribute-based cloud data integrity auditing protocol. The new protocol offers desirable properties namely attribute privacy-preserving and collusion-resistance. We prove soundness of our protocol based on the computational Diffie-Hellman assumption and the discrete logarithm assumption. Finally, we develop a prototype of the protocol which demonstrates the practicality of the protocol.

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Link to publisher version (DOI)

http://dx.doi.org/10.1109/TETC.2017.2759329