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IEEE Final Year Project Topic for CSE

Base Paper Title

Achieving Efficient and Privacy-Preserving Cross-Domain Big Data Deduplication in Cloud

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IEEE Project 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.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.

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