Structured encryption for knowledge graphs

Publication Type

Journal Article

Publication Date

8-2022

Abstract

We investigate the problem of structured encryption (STE) for knowledge graphs (KGs) where the knowledge of data can be efficiently and privately queried. Presently, the application of natural language processing (NLP) for knowledge-based search is gradually emerging. Compared with the traditional search based only on keywords of documents-symmetric searchable encryption (SSE), the knowledge-based search system transforms the latent knowledge contained in documents into a semantic network as a knowledge base, which greatly improves the accuracy and relevance of search results. In order to develop a knowledge-based search, the contents of documents are analyzed and extracted using KG techniques (e.g. multi-relational graph (MG) and property graph (PG)), and then all encrypted nodes and edges in a KG constitute the entire index table and database. This paper proposes the first STE for KGs with CQA2-security to search on protected knowledge, where KGs include MGs and PGs. In general, the latter is more complex than the former, but it can represent more abundant knowledge. Experimental results show that the index construction time of our schemes is about 1.9s and the query time is about 190 ms. Our sensitivity analysis shows that the performance of our proposed schemes is greatly influenced by the number of edges and nodes, but less by the number of properties. (C) 2022 Elsevier Inc. All rights reserved.

Keywords

Structured encryption, Knowledge search, Knowledge graph, Multi-relational graph, Property graph

Discipline

Databases and Information Systems | Data Storage Systems

Research Areas

Information Systems and Management

Publication

Information Sciences

Volume

605

First Page

43

Last Page

70

ISSN

0020-0255

Identifier

10.1016/j.ins.2022.05.015

Publisher

Elsevier

Additional URL

http://doi.org/10.1016/j.ins.2022.05.015

This document is currently not available here.

Share

COinS