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ESDedup: An efficient and secure deduplication scheme based on data similarity and blockchain for cloud-assisted medical storage systems

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Abstract

With the explosive growth of medical data, the tendency to store medical data on cloud is extremely widespread. However, large-scale medical data have put great pressure on cloud storage systems since redundancy of data wastes much storage space and increases economic expense. Besides, security is also highly important for medical data stored in the cloud. In order to reduce redundancy and ensure security of medical data simultaneously, this study proposes an efficient and secure deduplication scheme ESDedup. Compared with existing works, the redundancy of medical data is summarized and denoising of deduplication is firstly presented to decrease the storage overhead. Afterward, the rewriting algorithm based on similarity instead of the time attribute eliminates more fragments. Meanwhile, the auditing strategy of blockchain is designed to promote the auditing efficiency and security. Experiments demonstrate that ESDedup not only promotes the scalability and system performance, but also improves the deduplication ratio by 55.9% compared with the state-of-the-art method.

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Data availability

The datasets of Influenza, Pathogen and Interpro analyzed during the current study are available in the repositories of ftp://download.nmdc.cn/Influenza/, ftp://download.nmdc.cn/pathogen/, and ftp://download.nmdc.cn/interpro/, respectively. The Emotions dataset is available from PhysioNet but restrictions apply to the availability of the data, which was used under license for this study. The Emotions dataset is available from https://doi.org/10.13026/cdb3-8925 with the permission of PhysioNet.

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Acknowledgements

This research is supported by the National Key R &D Program of China (No.2018AAA0102100), the Fundamental Research Funds for the Central Universities of Central South University (No.2020zzts143), the Scientific and Technological Innovation Leading Plan of High-tech Industry of Hunan Province (No.2020GK2021).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, 410083, China

    Ling Xiao, Beiji Zou, Chengzhang Zhu & Fanbo Nie

  2. The College of Literature and Journalism, Central South University, Changsha, 410083, China

    Chengzhang Zhu

  3. Hunan Engineering Research Center of Machine Vision and Intelligent Medicine, Central South University, Changsha, 410083, China

    Ling Xiao, Beiji Zou, Chengzhang Zhu & Fanbo Nie

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  1. Ling Xiao
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  2. Beiji Zou
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Correspondence to Chengzhang Zhu.

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Xiao, L., Zou, B., Zhu, C. et al. ESDedup: An efficient and secure deduplication scheme based on data similarity and blockchain for cloud-assisted medical storage systems. J Supercomput 79, 2932–2960 (2023). https://doi.org/10.1007/s11227-022-04746-3

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