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

TL;DR

This paper introduces LRSE, a lightweight, efficient multi-keyword ranked search scheme over encrypted cloud data that uses dual word embeddings and an improved secure kNN to enhance privacy and retrieval precision.

Contribution

It proposes a novel lightweight ranked search scheme using dual word embeddings and an improved secure kNN, addressing privacy and efficiency in encrypted cloud data retrieval.

Findings

Supports top-k retrieval with higher precision

Ensures privacy protection according to security analysis

Demonstrates effectiveness on real-world datasets

Abstract

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.