数据与计算发展前沿 ›› 2023, Vol. 5 ›› Issue (5): 98-106.

CSTR: 32002.14.jfdc.CN10-1649/TP.2023.05.008

doi: 10.11871/jfdc.issn.2096-742X.2023.05.008

• 专刊:数据要素安全高效流通的关键技术 • 上一篇    下一篇

基于安全多方计算的隐私保护图查询

汤世源(),袁野*()   

  1. 北京理工大学，北京 100081
  • 收稿日期:2023-04-28 出版日期:2023-10-20 发布日期:2023-10-31
  • 通讯作者: 袁野（E-mail: yuan-ye@bit.edu.cn）
  • 作者简介:汤世源，北京理工大学，硕士研究生，主要研究方向为数据隐私保护、图数据管理与挖掘。
    本文中负责调查与分析、方法论的设计与实现、总体统稿。
    TANG Shiyuan is a postgraduate student at Beijing Institute of Technology, with a main research focus on data privacy protection, graph data management and mining.
    In this paper, he is responsible for investigating and analyzing, designing and implementing methodology, as well as overall coordination and drafting.
    E-mail: shiyuantg@gmail.com|袁野，北京理工大学，教授，博士，主要研究方向为大数据管理与分析、基于大数据的人工智能、分布式大数据计算。中国计算机学会（CCF）高级会员、美国计算机学会（ACM）高级会员、IEEE高级会；长期担任CCF A类会议(SIGMOD、VLDB、ICDE、KDD)的程序委员会委员。
    本文中负责总体研究目标和目的的制定、稿件审查。
    YUAN Ye is a professor and Ph.D. holder at Beijing Institute of Technology, with a main research focus on big data management and analysis, AI-based on big data, and distributed big data computing. He is a senior member of the China Computer Federation (CCF), the Association for Computing Machinery (ACM), and the Institute of Electrical and Electronics Engineers (IEEE). He has long served as a committee member of CCF A-class conferences (SIGMOD, VLDB, ICDE, KDD).
    In this paper, he is responsible for formulating the overall research objectives and purposes, as well as conducting manuscript review.
    E-mail: yuan-ye@bit.edu.cn
  • 基金资助:
    国家自然科学基金(61932004)

Privacy-Preserving Graph Query Based on Secure Multi-Party Computation

TANG Shiyuan(),YUAN Ye*()   

  1. Beijing Institute of Technology, Beijing 100081, China
  • Received:2023-04-28 Online:2023-10-20 Published:2023-10-31

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摘要：

【目的】 在互联网时代，图数据凭借着其丰富语义和结构信息，在众多的领域中发挥着独特的作用。同时，越来越多的公司选择使用“云服务” 作为基础设施平台，个人敏感数据的保护问题愈发受到人们的关注。这为隐私保护的图计算带来了严峻的挑战。【方法】 本文针对图计算中至关重要的子图匹配问题，首次提出了基于安全多方计算的图查询保护策略，将隐私保护图查询问题转化为关系表的安全连接问题，并根据图数据的特性对安全连接子协议进行改进。【结果】 相比于之前的隐私保护图查询工作，本文协议不仅提供了更低的计算和通讯开销，并且具有更高的安全保障性和可信度。

关键词: 安全多方计算, 云服务, 隐私保护, 图查询, 安全连接

Abstract:

[Objective] In the era of the Internet, graph data, with its rich semantics and structural information, plays a unique role in numerous fields. At the same time, more and more companies are choosing to use "cloud services" as their infrastructure platform, and the protection of personal sensitive data is increasingly attracting people's attention. This poses severe challenges to privacy-preserving graph computing. [Methods] This paper focuses on the crucial subgraph matching problem in graph computing and proposes, for the first time, a privacy-preserving graph query strategy based on secure multi-party computation. The privacy-preserving graph query problem is transformed into a secure join problem for relational tables, and the secure join sub-protocol is improved according to the characteristics of graph data. [Results] Compared with previous works on privacy-preserving graph query, our protocol not only provides lower computation and communication overhead, but also has higher security and credibility.

Key words: secure multi-party computation, cloud services, privacy preserving, graph query, secure join