期刊
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
卷 156, 期 -, 页码 163-175出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jpdc.2021.06.001
关键词
Shared data; Privacy preserving; Certificateless signatures; Cloud storage; Data dynamics
This paper proposes a certificateless privacy preserving public auditing scheme for dynamic shared data with group user revocation in cloud storage (CLPPPA), which protects data privacy from the verifier using a random masking technique. The scheme also supports shared data dynamics and group user revocation, while ensuring security under computational Diffie-Hellman (CDH) and discrete logarithm (DL) assumptions. The performance evaluation shows that CLPPPA achieves desirable efficiency.
With the increasing popularity of data sharing among users of a group in clouds, shared data auditing has become an important issue in the cloud auditing field. To address this issue, many shared data auditing schemes have been proposed in the literature based on either public key infrastructure (PKI) or identity-based cryptography (IBC). However, these schemes suffer from issues of complex certificate management or key escrow problem. To address these problems, recently, a certificateless shared auditing scheme was put forward. However, it cannot support data dynamics and does not protect data privacy against a verifier, i.e., the verifier can derive data content when verifying the data integrity, which affects the scheme's security. This paper proposes certificateless privacy preserving public auditing scheme for dynamic shared data with group user revocation in cloud storage (CLPPPA). CLPPPA protects the privacy of data from the verifier by leveraging a random masking technique. Further, CLPPPA also supports shared data dynamics and group user revocation. We formally prove the security of CLPPPA under computational Diffie-Hellman (CDH) and discrete logarithm (DL) assumptions in the Random Oracle Model (ROM). The performance of CLPPPA is evaluated by theoretical analysis, experimental results, and compared with the state-of-the-art ones. The results demonstrate that CLPPPA achieves desirable efficiency. (c) 2021 Elsevier Inc. All rights reserved.
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