  Cloud computing is emerging as a revolutionary computing paradigm which
pro-vides a flexible and economic strategy for data management and resource
sharing. Security and privacy become major concerns in the cloud scenario, for
which Searchable Encryption (SE) technology is proposed to support efficient
retrieval of encrypted data. However, the absence of lightweight ranked search
is still a typical shortage in existing SE schemes. In this paper, we propose a
Lightweight Efficient Multi-keyword Ranked Search over Encrypted Cloud Data
using Dual Word Embeddings (LRSE) scheme that supports top-k retrieval in the
known background model. For the first time, we formulate the privacy issue and
design goals for lightweight ranked search in SE. We employ word embedding
trained on the whole English Wikipedia using word2vec to replace the general
dictionary, afterwards we make use of Dual Embedding Space Model (DESM) to
substitute traditional Vector Space Model (VSM), based on which we achieve the
goal of lightweight ranked search with higher precision and solve the
challenging prob-lems caused by updating the traditional dictionary in existing
SE schemes. In LRSE, we employ an improved secure kNN scheme to guarantee
sufficient pri-vacy protection. Our security analysis shows that LRSE satisfies
our formulated privacy requirements and extensive experiments performed on
real-world datasets demonstrate that LRSE indeed accords with our proposed
design goals.
