Large universe ciphertext-policy attribute-based encryption with white-box traceability

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Abstract

A Ciphertext-Policy Attribute-Based Encryption (CP-ABE) system extracts the decryption keys over attributes shared by multiple users. It brings plenty of advantages in ABE applications. CP-ABE enables fine-grained access control to the encrypted data for commercial applications. There has been significant progress in CP-ABE over the recent years because of two properties called traceability and large universe, greatly enriching the commercial applications of CP-ABE. Traceability is the ability of ABE to track the malicious users or traitors who intentionally leak the partial or modified decryption keys to others for profits. Nevertheless, due to the nature of CP-ABE, it is difficult to identify the original key owner from an exposed key since the decryption privilege is shared by multiple users who have the same attributes. On the other hand, the property of large universe in ABE proposed by Lewko and Waters enlarges the practical applications by supporting flexible number of attributes. Several systems have been proposed to obtain either of the above properties. However, none of them achieve the two properties simultaneously in practice, which limits the commercial applications of CP-ABE to a certain extent. In this paper, we propose a practical large universe CP-ABE system supporting white-box traceability, which is suitable for commercial applications. Compared to existing systems, our new system has three advantages: (1) The number of attributes is not polynomially bounded; (2) Malicious users who leak their decryption keys could be traced; and, (3) The storage overhead for traitor tracing is constant. We also prove the selective security of our new system in the standard model under "q-type" assumption. © 2014 Springer International Publishing Switzerland.

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APA

Ning, J., Cao, Z., Dong, X., Wei, L., & Lin, X. (2014). Large universe ciphertext-policy attribute-based encryption with white-box traceability. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8713 LNCS, pp. 55–72). Springer Verlag. https://doi.org/10.1007/978-3-319-11212-1_4

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