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Link to original content: https://doi.org/10.1007/s12083-018-0691-8
Secure, flexible and high-efficient similarity search over encrypted data in multiple clouds | Peer-to-Peer Networking and Applications Skip to main content
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Secure, flexible and high-efficient similarity search over encrypted data in multiple clouds

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Abstract

In cloud computing, a cloud user can improve a system’s reliability by utilizing services from multiple clouds, known as the multi-cloud service model. This model, however, causes serious security concerns since multi-clouds increase the chance of being attacked. To maintain data security, a commonly-used strategy is to encrypt data stored in clouds. Yet, issues such as inconsistent data storage (Byzantine faults), dynamic data update, and inefficient data retrieval among multiple clouds, have remained as open challenges. In this paper, we propose a multi-cloud secure similarity search (MC3S) method to effectively and efficiently utilize the encrypted data over multiple clouds. To achieve secure, flexible and high-efficient data search, MC3S utilizes two novel data structures, called gram-filter and prefix-ring. Different from prior works in secure data search, we address the problem of Byzantine faults among multiple clouds, and our algorithm enables dynamic data index insertion and revocation without decryption. We prove that MC3S can resist chosen-keyword attacks and achieve non-adaptive semantic security. Finally, we evaluate the efficiency of MC3S with real world datasets.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No.61702321, 61772327, 61602295, 61572311, 61872230), the Excellent Young Teachers Program of Shanghai (No. ZZsdl15152), the Project of Shanghai Science and Technology Committee (No. 15110500700), the Dawn Program of Shanghai Education Commission (No. 16SG47).

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Authors and Affiliations

  1. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, 200090, China

    Jinguo Li, Mi Wen, Kui Wu, Kejie Lu, Fengyong Li & Hongjiao Li

  2. Computer Science Department, University of Victoria, Victoria, BC, V8W 3P6, Canada

    Kui Wu

  3. Department of Electrical and Computer Engineering, University of Puerto Rico, Puerto Rico, 00681, USA

    Kejie Lu

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  1. Jinguo Li
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Correspondence to Jinguo Li.

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Li, J., Wen, M., Wu, K. et al. Secure, flexible and high-efficient similarity search over encrypted data in multiple clouds. Peer-to-Peer Netw. Appl. 12, 893–911 (2019). https://doi.org/10.1007/s12083-018-0691-8

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