Zero knowledge based data deduplication using in-line Block Matching protocol for secure cloud storage

Abstract

In the area of cloud computing, data deduplication enables the cloud server to store a single copy of data by eliminating redundant files to improve storage and network efficiency. Proof-of-Ownership (PoW) is a cryptographic function that verifies the user who really owns the data. Most of the existing schemes have tried to solve the deduplication problem by providing the same encryption key for identical data. However, these schemes suffer from dynamic changes in ownership management. In this paper, we propose a In-line Block Matching (IBM) protocol based on zero-knowledge proof for deduplication with dynamic ownership management, which eliminates the unauthorized access of sensitive data.In this proposed work, for a new file, the uploader randomly chooses a file encryption key and encrypts the file. The user also computes a unique proof for the uploaded file by dividing the file into number of blocks and stores this proof to the cloud server. The cloud server computes the group key for the ciphertext and re-encrypts it using this group key.The cloud server also does the proof verification against the subsequent uploader for an existing file. The cloud server is honest-but-curious, so the proposed scheme confirms that the cloud server does not know any information about file encryption key even though it plays a proxy role. The result shows that our proposed scheme protects the data from both cloud server and adversaries. Also the computational cost is comparatively less than other existing schemes.

Deep neural network-based security awareness research for private data cloud storage

“…Based on the calculation results, a number of data chunks are then selected using the van der Mond matrix set and the neuron network is adjusted according to the security level requirements of the cloud storage security-aware approach until the step of data chunking and coding switching is completed [13]. After the operation of privacy data chunking encoding is completed, encryption measures need to be applied to each data chunk using the mainstream function secondary encryption to facilitate the smooth operation of the privacy data security-aware model [14][15]. Based on the above description, the steps for building a data chunking encoding model based on deep neural networks are completed.…”

Survey of image deduplication for cloud storage

“…Authors of [27] propose an in-line block matching-based data deduplication scheme with dynamic user management. Users encrypt their data using convergent encryption.…”