LRSE: A Lightweight Efficient Searchable Encryption Scheme using Local and Global Representations

TL;DR

LRSE is a new lightweight searchable encryption scheme that integrates machine learning with local and global data representations to improve search quality and privacy in cloud environments.

Contribution

It introduces a novel combination of machine learning and local/global data representations into searchable encryption, enhancing search quality and efficiency.

Findings

Achieves state-of-the-art search quality in experiments.

Maintains low system cost and high privacy protection.

Employs an improved secure kNN scheme for privacy.

Abstract

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.