Secure Multi-keyword Ranked Search over Encrypted Cloud Data

Secure Multi-keyword Ranked Search over Encrypted Cloud Data for Multiple Data

Secure multi-keyword ranked search over outsourced cloud data has become a hot research field. Most existing works follow the model of “Single Owner”, which just supports searching on the outsourced data belong to only one data owner. But the more realistic scenario is “Multiple Owners”, users could search on all datasets outsourced by different data owners. However, directly extending “Single Owner” schemes into “Multiple Owners” scenario still face the major two challenges: (1) the inconvenient key management and the resulting high communication cost; (2) due to the different authorities of owners, the qualities of documents are also different even if they are about the similar topic, but current rank functions in this area cannot rank documents based on their qualities. In this paper, we propose a secure multi-keyword ranked search scheme for multiple data owners. A trusted third party is imported to solve the problem of key management. We exploit the vector space model for generating index and query, and our new-designed KDO algorithm is utilized for providing keyword weight, so that the rank function not only considers about the relevance between query and document, but also takes into account the document quality. In order to protect privacy for both owners and users, the Asymmetric Scalar-product Preserving Encryption approach is utilized for encrypting weighted index and query. Besides, we construct the Grouped Balanced Binary tree index, which could further improve the search efficiency by Greedy Depth-first search algorithm. Extensive experiments demonstrate that our proposed scheme is secure, accurate and efficient.

see more at https://www.sciencedirect.com/science/article/pii/S0164121217303011