面向多方数据融合分析的隐私计算技术综述

刘圣龙; 黄秀丽; 江伊雯; 姜嘉伟; 田月池; 周泽峻; 牛犇

doi:10.11959/j.issn.2096-109x.2024078

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面向多方数据融合分析的隐私计算技术综述

综述 | 更新时间：2025-02-06

- 面向多方数据融合分析的隐私计算技术综述
- Review of privacy computing techniques for multi-party data fusion analysis
- 网络与信息安全学报 2024年10卷第6期页码：24-36
- 作者机构：
  
  1.国家电网有限公司大数据中心，北京 100052
  2.国网智能电网研究院有限公司电力网络安全防护与监测技术实验室，江苏南京 210000
  3.中国科学院信息工程研究所，北京 100085
  4.网络空间安全防御重点实验室，北京 100085
  5.中国科学院大学网络空间安全学院，北京 100049
- 作者简介：
  
  [ "刘圣龙（1988- ），男，辽宁沈阳人，国家电网有限公司大数据中心高级工程师，主要研究方向为网络与数据安全。" ]
  [ "黄秀丽（1979- ），女，河南开封人，国网智能电网研究院有限公司高级工程师，主要研究方向为电力数据安全研究。" ]
  [ "江伊雯（1993- ），女，安徽宣城人，国家电网有限公司大数据中心高级工程师，主要研究方向为数据安全。" ]
  [ "姜嘉伟（1994- ），男，吉林吉林人，国家电网有限公司大数据中心工程师，主要研究方向为网络安全。" ]
  [ "田月池（1998- ），女，河北保定人，中国科学院信息工程研究所博士生，主要研究方向为隐私计算、隐私保护效果评估。" ]
  [ "周泽峻（2000- ），男，河南洛阳人，中国科学院信息工程研究所博士生，主要研究方向为隐私计算、数据安全。" ]
  [ "牛犇（1984- ），男，陕西西安人，博士，中国科学院信息工程研究所研究员、博士生导师，主要研究方向为数据安全、隐私计算。" ]
- 基金信息：
  
  国家电网有限公司总部管理科技项目(5108-202218280A-2-393-XG)
- DOI：10.11959/j.issn.2096-109x.2024078
  中图分类号： TP393
- 收稿：2024-04-30，
  
  修回：2024-10-15，
  
  纸质出版：2024-12-25
- 稿件说明：
移动端阅览
刘圣龙, 黄秀丽, 江伊雯, 等. 面向多方数据融合分析的隐私计算技术综述[J]. 网络与信息安全学报, 2024, 10(6): 24-36.

LIU S L, HUANG X L, JIANG Y W, et al. Review of privacy computing techniques for multi-party data fusion analysis[J]. Chinese Journal of Network and Information Security, 2024, 10(6): 24-36.
刘圣龙, 黄秀丽, 江伊雯, 等. 面向多方数据融合分析的隐私计算技术综述[J]. 网络与信息安全学报, 2024, 10(6): 24-36. DOI： 10.11959/j.issn.2096-109x.2024078.

LIU S L, HUANG X L, JIANG Y W, et al. Review of privacy computing techniques for multi-party data fusion analysis[J]. Chinese Journal of Network and Information Security, 2024, 10(6): 24-36. DOI： 10.11959/j.issn.2096-109x.2024078.

摘要

数据时代，泛在共享环境下个人隐私信息面临的威胁十分广泛，如App频繁超范围采集个人信息、大数据杀熟等，跨系统交换的多方隐私计算需求迫切。因此，聚焦面向泛在共享环境下跨系统交换的多方隐私计算需求，以多方数据融合中隐私数据的安全共享和受控传播应用需求为切入点，从多方隐私计算、多方隐私信息共享控制、多方数据协同安全计算等方面梳理了现有相关工作，进行综述。分析了泛在共享环境下个人隐私信息保护的背景和研究现状。综述并对比分析了近年来多方隐私计算、多方隐私信息共享控制、多方数据协同安全计算等方面国内外最新研究成果，针对多方隐私计算，介绍了全生命周期隐私保护、隐私信息流转控制、敏感数据安全交换等技术；针对多方隐私信息共享控制，介绍了本地控制、延伸控制和脱敏控制三类技术；针对多方数据协同安全计算，介绍了学术界、产业界常用的技术。最后，对多方隐私计算面临的挑战和发展方向进行展望，传统的基于匿名、加扰、访问控制等技术的隐私脱敏方案、基于密码学的方案、基于联邦学习的方案等仍具有局限性，而隐私计算理论给出的面向全生命周期保护的计算框架和信息系统框架，需结合不同应用场景，践行隐私信息全生命周期保护。

Abstract

In the data era

threats to personal privacy information in ubiquitous sharing environments are widespread

such as apps frequently collecting personal information beyond scope

and big data-enabled price discrimination against frequent customers. The need for multi-party privacy computing for cross-system exchanges is urgent. This work focused on the needs of multi-party privacy computing for cross-system exchanges in ubiquitous sharing environments

taking the security sharing and controlled dissemination of private data in multi-party data fusion applications as the starting point

and provided reviews of existing relevant work from the perspectives of multi-party privacy computing

multi-party privacy information sharing control

and multi-party data collaborative secure computing. First

the background and research status of personal privacy information protection in a ubiquitous sharing environment were analyzed. Then

the latest domestic and foreign research results in recent years regarding multi-party privacy computing

multi-party privacy information sharing control

and multi-party data collaborative security computing were reviewed and comparatively analyzed. Regarding multi-party privacy computing

technologies such as full lifecycle privacy protection

privacy information flow control

and secure exchange of sensitive data were introduced. In terms of multi-party privacy information sharing control

localized control

extended control

and anonymization control techniques were discussed. In the aspect of multi-party data collaborative secure computing

commonly used techniques in both academia and industry were discussed. Finally

the challenges and development directions of multi-party privacy computing were prospected. There were still limitations for anonymity

scrambling

or access control-based traditional privacy desensitization measures

cryptography-based measures

and federated learning-based measures

while privacy computing theory provided a computational and information system framework for full-lifecycle protection

which needed to be combined with different application scenarios to implement full-lifecycle privacy information protection.

关键词

Keywords

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