Enabling Privacy-Preserving and Efficient Authenticated Graph Queries on Blockchain-Assisted Clouds

Haotian Wu, Zecheng Li, Rui Song, Bin Xiao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Prior research has introduced a new scenario of blockchain-assisted clouds where the data owner outsources original data to cloud servers and stores some metadata on the blockchain. Despite some research on key-value query and range query in this hybrid-storage scenario, other more complicated data types are not yet supported. In this article, we conduct pioneering research on authenticated queries for graph data, which is a popular data type such as the knowledge graph data, on the blockchain-assisted cloud. The primary challenge is how to design an authenticated data structure (ADS) that supports authenticated queries and can be easily maintained by the blockchain. To this end, we propose a novel ADS, named PAGB, based on the RSA accumulator and completeness set. It can also prevent the original data from being revealed to the public through blockchain or irrelevant queries. We further optimize our design to be more efficient in terms of communication and computation. The effectiveness and efficiency of PAGB are verified through theoretical analysis and extensive experiments.

Original languageEnglish
Pages (from-to)9728-9742
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Volume35
Issue number9
DOIs
Publication statusPublished - 1 Sept 2023

Keywords

  • authenticated query
  • blockchain
  • cloud data query
  • knowledge graph data
  • Privacy-preserving

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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