Journal of Information Security Reserach ›› 2023, Vol. 9 ›› Issue (8): 714-.

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Key Technologies and Research Prospects of Privacy Computing


  • Online:2023-08-01 Published:2023-09-04


沈传年, 徐彦婷, 陈滢霞   

  1. (国家计算机网络应急技术处理协调中心上海分中心上海201315)
  • 通讯作者: 沈传年 硕士,工程师.主要研究方向为网络与信息安全、区块链.
  • 作者简介:沈传年 硕士,工程师.主要研究方向为网络与信息安全、区块链. 徐彦婷 硕士,工程师.主要研究方向为人工智能、数据安全. 陈滢霞 硕士,助理工程师.主要研究方向为网络与信息安全、推荐算法.

Abstract: Privacy computing, as an important technical means taking into account both data circulation and privacy protection, can effectively break the “data island” barriers while ensuring data security, it enables open data sharing, and promotes the deep mining and use of data and crossdomain integration. In this paper, the background knowledge, basic concepts and architecture of privacy computing were introduced, the basic concepts of three key technologies of privacy computing, including secure multiparty computation, federated learning and trusted execution environment were elaborated, and studies on the existing privacy security was conducted, a multidimensional comparison and summarization of the differences of the three key technologies were made. On this basis, the future research direction of privacy computing is prospected from the technical integration of privacy computing with blockchain, deep learning and knowledge graph.

Key words: privacy computing, secure multiparty computation, federated learning, trusted execution environment, blockchain

摘要: 隐私计算作为目前兼顾数据流通和隐私保护的重要技术手段,能在确保数据安全的同时有效打破“数据孤岛”壁垒,实现数据开放共享,促进数据的深度挖掘使用和跨领域融合.介绍隐私计算的背景知识、基本概念以及体系架构,分别阐述隐私计算3种关键技术安全多方计算、联邦学习和可信执行环境的基本概念,对其存在的隐私安全进行研究,并对3种关键技术的差异进行多维度的比较总结.在此基础上,以隐私计算与区块链、深度学习、知识图谱的技术融合为出发点对隐私计算的未来研究方向进行展望.

关键词: 隐私计算, 安全多方计算, 联邦学习, 可信执行环境, 区块链