Issue No. 08 - Aug. (2012 vol. 23)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2011.282
Cong Wang , Illinois Institute of Technology, Chicago
Ning Cao , Worcester Polytechnic Institute, Worcester
Kui Ren , Illinois Institute of Technology, Chicago
Wenjing Lou , Virginia Polytechnic Institute and State University, Falls Church
Cloud computing economically enables the paradigm of data service outsourcing. However, to protect data privacy, sensitive cloud data have to be encrypted before outsourced to the commercial public cloud, which makes effective data utilization service a very challenging task. Although traditional searchable encryption techniques allow users to securely search over encrypted data through keywords, they support only Boolean search and are not yet sufficient to meet the effective data utilization need that is inherently demanded by large number of users and huge amount of data files in cloud. In this paper, we define and solve the problem of secure ranked keyword search over encrypted cloud data. Ranked search greatly enhances system usability by enabling search result relevance ranking instead of sending undifferentiated results, and further ensures the file retrieval accuracy. Specifically, we explore the statistical measure approach, i.e., relevance score, from information retrieval to build a secure searchable index, and develop a one-to-many order-preserving mapping technique to properly protect those sensitive score information. The resulting design is able to facilitate efficient server-side ranking without losing keyword privacy. Thorough analysis shows that our proposed solution enjoys “as-strong-as-possible” security guarantee compared to previous searchable encryption schemes, while correctly realizing the goal of ranked keyword search. Extensive experimental results demonstrate the efficiency of the proposed solution.
Ranked search, searchable encryption, order-preserving mapping, confidential data, cloud computing.
C. Wang, W. Lou, N. Cao and K. Ren, "Enabling Secure and Efficient Ranked Keyword Search over Outsourced Cloud Data," in IEEE Transactions on Parallel & Distributed Systems, vol. 23, no. , pp. 1467-1479, 2011.