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Journal of Computer Science and Technology

, Volume 34, Issue 1, pp 133–154 | Cite as

Secure Inverted Index Based Search over Encrypted Cloud Data with User Access Rights Management

  • Fateh BoucennaEmail author
  • Omar Nouali
  • Samir Kechid
  • M. Tahar Kechadi
Regular Paper
  • 14 Downloads

Abstract

Cloud computing is a technology that provides users with a large storage space and an enormous computing power. However, the outsourced data are often sensitive and confidential, and hence must be encrypted before being outsourced. Consequently, classical search approaches have become obsolete and new approaches that are compatible with encrypted data have become a necessity. For privacy reasons, most of these approaches are based on the vector model which is a time consuming process since the entire index must be loaded and exploited during the search process given that the query vector must be compared with each document vector. To solve this problem, we propose a new method for constructing a secure inverted index using two key techniques, homomorphic encryption and the dummy documents technique. However, 1) homomorphic encryption generates very large ciphertexts which are thousands of times larger than their corresponding plaintexts, and 2) the dummy documents technique that enhances the index security produces lots of false positives in the search results. The proposed approach exploits the advantages of these two techniques by proposing two methods called the compressed table of encrypted scores and the double score formula. Moreover, we exploit a second secure inverted index in order to manage the users’ access rights to the data. Finally, in order to validate our approach, we performed an experimental study using a data collection of one million documents. The experiments show that our approach is many times faster than any other approach based on the vector model.

Keywords

searchable encryption cloud computing homomorphic encryption attribute-based encryption inverted index 

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Notes

Acknowledgement(s)

We wish to express our gratitude to Dr. Djamal Belazzougui and Mr. Mohamed Saddek Derki for their help to improve this work. We also thank the anonymous reviewers and the editors for their constructive suggestions and remarks.

Supplementary material

11390_2019_1903_MOESM1_ESM.pdf (228 kb)
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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Fateh Boucenna
    • 1
    • 2
    Email author
  • Omar Nouali
    • 1
  • Samir Kechid
    • 2
  • M. Tahar Kechadi
    • 3
  1. 1.Security Division, Research Center for Scientific and Technical InformationAlgiersAlgeria
  2. 2.Laboratory of Research in Artificial Intelligence, Department of Computer ScienceUniversity of Sciences and Technology Houari BoumedieneAlgiersAlgeria
  3. 3.The Insight CentreUniversity College DublinDublinIreland

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