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Cloud computing is emerging as a revolutionary computing paradigm whichpro-vides a flexible and economic strategy for data management and resourcesharing. Security and privacy become major concerns in the cloud scenario, forwhich Searchable Encryption (SE) technology is proposed to support efficientretrieval of encrypted data. However, the absence of lightweight ranked searchis still a typical shortage in existing SE schemes. In this paper, we propose aLightweight Efficient Multi-keyword Ranked Search over Encrypted Cloud Datausing Dual Word Embeddings (LRSE) scheme that supports top-k retrieval in theknown background model. For the first time, we formulate the privacy issue anddesign goals for lightweight ranked search in SE. We employ word embeddingtrained on the whole English Wikipedia using word2vec to replace the generaldictionary, afterwards we make use of Dual Embedding Space Model (DESM) tosubstitute traditional Vector Space Model (VSM), based on which we achieve thegoal of lightweight ranked search with higher precision and solve thechallenging prob-lems caused by updating the traditional dictionary in existingSE schemes. In LRSE, we employ an improved secure kNN scheme to guaranteesufficient pri-vacy protection. Our security analysis shows that LRSE satisfiesour formulated privacy requirements and extensive experiments performed onreal-world datasets demonstrate that LRSE indeed accords with our proposeddesign goals.

Lightweight Efficient Multi-keyword Ranked Search over Encrypted Cloud Data using Dual Word Embeddings