LRSE: A Lightweight Efficient Searchable Encryption Scheme using Local and Global Representations
This addresses security and privacy concerns in cloud computing by enabling efficient retrieval of encrypted data, though it appears incremental as it builds on existing SE schemes with novel method integration.
The paper tackles the problem of achieving lightweight ranked search with high search quality in searchable encryption for cloud data, proposing LRSE which integrates machine learning and combines local and global representations, resulting in state-of-the-art search quality with the lowest system cost as demonstrated in experiments.
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.