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Privacy-preserving multi-keyword text search in the cloud supporting similarity-based ranking

Published: 08 May 2013 Publication History

Abstract

With the increasing popularity of cloud computing, huge amount of documents are outsourced to the cloud for reduced management cost and ease of access. Although encryption helps protecting user data confidentiality, it leaves the well-functioning yet practically-efficient secure search functions over encrypted data a challenging problem. In this paper, we present a privacy-preserving multi-keyword text search (MTS) scheme with similarity-based ranking to address this problem. To support multi-keyword search and search result ranking, we propose to build the search index based on term frequency and the vector space model with cosine similarity measure to achieve higher search result accuracy. To improve the search efficiency, we propose a tree-based index structure and various adaption methods for multi-dimensional (MD) algorithm so that the practical search efficiency is much better than that of linear search. To further enhance the search privacy, we propose two secure index schemes to meet the stringent privacy requirements under strong threat models, i.e., known ciphertext model and known background model. Finally, we demonstrate the effectiveness and efficiency of the proposed schemes through extensive experimental evaluation.

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cover image ACM Conferences
ASIA CCS '13: Proceedings of the 8th ACM SIGSAC symposium on Information, computer and communications security
May 2013
574 pages
ISBN:9781450317672
DOI:10.1145/2484313
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 May 2013

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Author Tags

  1. cloud computing
  2. multi-keyword search
  3. privacy-preserving search
  4. similarity-based ranking

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ASIA CCS '13 Paper Acceptance Rate 35 of 216 submissions, 16%;
Overall Acceptance Rate 418 of 2,322 submissions, 18%

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  • (2024)Secure Similarity Queries Over Vertically Distributed Data via TEE-Enhanced Cloud ComputingIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.341363019(6237-6251)Online publication date: 2024
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