  Cloud computing is emerging as a revolutionary computing paradigm, while
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
with higher search quality in a harsh adversary model is still a typical
shortage in existing SE schemes. In this paper, we propose a novel SE scheme
called LRSE which firstly integrates machine learning methods into the
framework of SE and combines local and global representations of encrypted
cloud data to achieve the above design goals. In LRSE, we employ an improved
secure kNN scheme to guarantee sufficient privacy protection. Our detailed
security analysis shows that LRSE satisfies our formulated privacy
requirements. Extensive experiments performed on benchmark datasets demonstrate
that LRSE indeed achieves state-of-the-art search quality with lowest system
cost.
