Achieving efficient and privacy preserving cross-domain big data deduplication in cloud

1. 2020 – 2021
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Achieving Efficient and Privacy-Preserving Cross-Domain Big Data Deduplication
in Cloud
Abstract:
Secure data deduplication, as it can eliminate redundancies over encrypted data, has been
widely developed in cloud storage. Among them, the convergent encryption has been
extensively adopted. However, it is vulnerable to brute-force attacks that determine which
plaintext in a message space corresponds to a given ciphertext. Many existing schemes have
to sacrifice efficiency to resist brute-force attacks, especially for cross-domain deduplication,
which is inevitably contrary to practical applications. Moreover, few existing schemes
consider protecting the message equality information (whether two ciphertexts correspond to
an identical plaintext). In this paper, we propose an efficient and privacy-preserving
deduplication scheme. Specifically, by generating a random tag and a constant number of
random ciphertexts for each data, our scheme not only ensures data confidentiality under
multi-domain deduplication but also resists brute-force attacks. By allowing only the agent
and cloud to perform intra-deduplication and inter-deduplication, respectively, our scheme
minimizes the disclosure of the message equality information. Detailed security analysis
shows that our scheme achieves privacy-preservation for data content and the message
equality information, data integrity while resisting brute-force attacks. Furthermore, extensive
simulations demonstrate that our scheme outperforms the existing competing schemes,
especially the computational cost and the time complexity of the duplicate search.