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

  • Authors:
  • Wenhai Sun;Bing Wang;Ning Cao;Ming Li;Wenjing Lou;Y. Thomas Hou;Hui Li

  • Affiliations:
  • The State Key Laboratory of Integrated Services Networks, Xidian University & Virginia Tech, Xi'an, China;Virginia Tech, Falls Church, VA, USA;Worcester Polytechnic Institute, Worcester, MA, USA;Utah State University, Logan, UT, USA;Virginia Tech, Falls Church, VA, USA;Virginia Tech, Blacksburg, VA, USA;The State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, China

  • Venue:
  • Proceedings of the 8th ACM SIGSAC symposium on Information, computer and communications security
  • Year:
  • 2013

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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.