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Countering Statistical Attacks in Cloud-Based Searchable Encryption

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Abstract

Searchable encryption (SE) is appearing as a prominent solution in the intersection of privacy protection and efficient retrieval of data outsourced to cloud computing storage. While it preserves privacy by encrypting data, yet supports search operation without data leakage. Due to its applicability, many research communities have proposed different SE schemes under various security definitions with numerous customary features (i.e. multi keyword search, ranked search). However, by reason of multi-keyword ranked search, SE discloses encrypted document list corresponding to multiple (secure) query keywords (or trapdoor). Such disclosure of statistical information helps an attacker to analyze and deduce the content of the data. To counter statistical information leakage in SE, we propose a scheme referred to as Countering Statistical Attack in Cloud based Searchable Encryption (CSA-CSE) that resorts to randomness in all components of an SE. CSA-CSE adopts inverted index that is built with a hash digest of a pair of keywords. Unlike existing schemes, ranking factors (i.e. relevance scores) rank the documents and then they no longer exist in the secure index (neither in order preserving encrypted form). Query keywords are also garbled with randomness in order to hide actual query/result statistics. Our security analysis and experiment on request for comments database ensure the security and efficiency of CSA-CSE.

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  • 29 August 2018

    The original article has been published with an incorrect grant number in the acknowledgements which should be RG # 1439-036.

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Acknowledgements

This research work was partially supported by the Faculty of Computer Science and Information Technology, University of Malaya under a special allocation of the Post Graduate Fund for RP036 (A, B, C)-15AET project. This work is also supported by the Deanship of Scientific Research at King Saud University through Research Group number RG-1435-051.

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Authors and Affiliations

  1. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    M. A. Manazir Ahsan, Ihsan Ali & Mohd Yamani Idna Bin Idris

  2. College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

    Muhammad Imran & Muhammad Shoaib

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  1. M. A. Manazir Ahsan
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  2. Ihsan Ali
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Correspondence to Ihsan Ali.

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Ahsan, M.A.M., Ali, I., Bin Idris, M.Y.I. et al. Countering Statistical Attacks in Cloud-Based Searchable Encryption. Int J Parallel Prog 48, 470–495 (2020). https://doi.org/10.1007/s10766-018-0584-8

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