An applied study of privacy preservation on government affairs based on federated unlearning

XIAO Chuqiao; XUE Lide; NI Xiong; LIU Zhusen; YANG Hao; CHEN Mutong

doi:10.11959/j.issn.2096-0271.2025087

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An applied study of privacy preservation on government affairs based on federated unlearning

更新时间：2026-02-27

- An applied study of privacy preservation on government affairs based on federated unlearning
- BIG DATA RESEARCH Pages: 1-16(2026)
- 作者机构：
  
  1.上海市大数据中心，上海 200072
  2.北京航空航天大学杭州创新研究院，浙江杭州 310016
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2025087
  CLC： TP309
- Received：09 July 2025，
  
  Online First：25 February 2026，
- 稿件说明：
移动端阅览
肖楚乔,薛立德,倪雄等.基于联邦遗忘学习的公共数据隐私保护应用研究[J].大数据,

XIAO Chuqiao,XUE Lide,NI Xiong,et al.An applied study of privacy preservation on government affairs based on federated unlearning[J].BIG DATA RESEARCH,
肖楚乔,薛立德,倪雄等.基于联邦遗忘学习的公共数据隐私保护应用研究[J].大数据, DOI：10.11959/j.issn.2096-0271.2025087.

XIAO Chuqiao,XUE Lide,NI Xiong,et al.An applied study of privacy preservation on government affairs based on federated unlearning[J].BIG DATA RESEARCH, DOI：10.11959/j.issn.2096-0271.2025087.

摘要

当前，数据已成为重要的生产要素。如何在数据流通过程中保护数据隐私已经成为研究重点。联邦遗忘学习（federated unlearning，FU）可以撤销数据对联邦学习（federated learning，FL）模型的影响，进一步保护公民的隐私。探索FU技术在政务领域的应用具有极高的社会研究价值。首先，分析了FU技术的价值和在政务领域探索的意义；接着，总结了FL和FU的技术特点和最新进展；之后，对基于FU的政务领域应用场景进行了分析；最后，展望了政务领域使用FU技术的未来研究方向。

Abstract

Data has now become one of the key factors of production. Protecting data privacy during data circulation has emerged as a critical research focus. Federated Unlearning (FU) can effectively undo the influence of data on federated learning (FL) models

thereby further safeguarding user privacy. Exploring the application of FU technology in the government affairs domain holds significant social research value. The paper introduces the value proposition of FU technology and the importance of investigating its governmental applications. It then presents the technical characteristics and latest developments of FL and FU. Thirdly

it analyzes the application scenarios of FU technology in the government affairs domain. Finally

it outlines future research directions for utilizing FU technology in a governmental context.

关键词

Keywords

references

中共中央国务院关于构建更加完善的要素市场化配置体制机制的意见 [J ] . 社会主义论坛 , 2020 ( 5 ): 4 - 6 .

Opinions of the Central Committee of the Communist Party of China and the State Council on building a more comprehensive market-based allocation system and mechanism for factors [J ] . Journal of Socialist Forum , 2020 ( 5 ): 4 - 6 .

王跃 , 莫莉娟 , 苏娜 . 面向违规导出风险的政务数据流通安全治理体系研究 [J ] . 大数据 , 2024 , 10 ( 6 ): 138 - 148 .

WANG Y , MO L J , SU N . Research on the security governance system for the circulation of government data facing the risk of illegal export [J ] . Big Data Research , 2024 , 10 ( 6 ): 138 - 148 .

中国信息通信研究院 . 政务数据共享开放安全研究报告 [R ] . 2021 .

China Academy of Information and Communications Technology (CAICT) . Research report on government data sharing and opening [R ] . 2021 .

钱学胜 , 吴寰宇 , 陈诚 , 等 . 基于联邦学习的新冠肺炎疫情跨区域智慧防控技术: 以上海市实践为例 [J ] . 科技导报 , 2021 , 39 ( 24 ): 96 - 107 .

QIAN X S , WU H Y , CHEN C , et al . Cross-region smart COVID-19 pandemic management and control based on federated learning: with Shanghai as an example [J ] . Science & Technology Review , 2021 , 39 ( 24 ): 96 - 107 .

张燕 , 杨一帆 , 伊人 , 等 . 隐私计算场景下数据质量治理探索与实践 [J ] . 大数据 , 2022 , 8 ( 5 ): 55 - 73 .

ZHANG Y , YANG Y F , YI R , et al . Exploration and practice of data quality governance in privacy computing scenarios [J ] . Big Data Research , 2022 , 8 ( 5 ): 55 - 73 .

吴坚平 , 陈超超 , 金加和 , 等 . 基于联邦学习的政务大数据平台应用研究 [J ] . 大数据 , 2024 , 10 ( 3 ): 40 - 54 .

WU J P , CHEN C C , JIN J H , et al . Research on application of government big data platform based on federated learning [J ] . Big Data Research , 2024 , 10 ( 3 ): 40 - 54 .

LIU Z Y , JIANG Y , SHEN J Y , et al . A survey on federated unlearning: challenges, methods, and future directions [J ] . ACM Computing Surveys , 2025 , 57 ( 1 ): 1 - 38 .

General data protection regulation (GDPR) compliance guidelines [Z ] . 2025 .

GOLDMAN E . An introduction to the California consumer privacy act (CCPA) [J ] . SSRN Electronic Journal , 2018 : 3211013 .

Art . 17 GDPR - right to erasure (‘right to be forgotten’) [Z ] . 2018 .

ROMANDINI N , MORA A , MAZZOCCA C , et al . Federated unlearning: a survey on methods, design guidelines, and evaluation metrics [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2025 , 36 ( 7 ): 11697 - 11717 .

LIU Y , XU L , YUAN X L , et al . The right to be forgotten in federated learning: an efficient realization with rapid retraining [EB ] . arXiv preprint , 2022 , arXiv: 2203.07320 .

JIANG Y , SHEN J Y , LIU Z Y , et al . Toward efficient and certified recovery from poisoning attacks in federated learning [J ] . IEEE Transactions on Information Forensics and Security , 2025 , 20 : 2632 - 2647 .

CAO X Y , JIA J Y , ZHANG Z X , et al . FedRecover: recovering from poisoning attacks in federated learning using historical information [C ] // Proceedings of the 2023 IEEE Symposium on Security and Privacy . Piscataway : IEEE Press , 2023 : 1366 - 1383 .

QIU H Y , WANG Y W , XU Y H , et al . FedCIO: efficient exact federated unlearning with clustering, isolation, and one-shot aggregation [C ] // Proceedings of the 2023 IEEE International Conference on Big Data . Piscataway : IEEE Press , 2023 : 5559 - 5568 .

GUO X T , WANG P F , QIU S , et al . FAST: adopting federated unlearning to eliminating malicious terminals at server side [J ] . IEEE Transactions on Network Science and Engineering , 2024 , 11 ( 2 ): 2289 - 2302 .

TAO Y M , WANG C L , PAN M , et al . Communication efficient and provable federated unlearning [J ] . Proceedings of the VLDB Endowment , 2024 , 17 ( 5 ): 1119 - 1131 .

WANG P F , SONG W , QI H , et al . Server-initiated federated unlearning to eliminate impacts of low-quality data [J ] . IEEE Transactions on Services Computing , 2024 , 17 ( 3 ): 1196 - 1211 .

清华大学人工智能研究院知识工程研究中心 , 北京智谱华章科技有限公司 , 开放群岛开源社区 . 2023联邦学习全球研究与应用趋势报告 [C ] // 第32届国际人工智能联合会议(IJCAI) . 澳门 , 2023 .

Knowledge Engineering Research Center , Institute for Artificial Intelligence , Tsinghua University ; Beijing Zhipu Huazhang Technology Co. , Ltd. ; Open Islands . 2023 global research and application trends report on federated learning [C ] // Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) . Macau , 2023 .

YANG Q , LIU Y , CHEN T J , et al . Federated machine learning [J ] . ACM Transactions on Intelligent Systems and Technology , 2019 , 10 ( 2 ): 1 - 19 .

LI Q B , WEN Z Y , WU Z M , et al . A survey on federated learning systems: vision, hype and reality for data privacy and protection [J ] . IEEE Transactions on Knowledge and Data Engineering , 2023 , 35 ( 4 ): 3347 - 3366 .

LIU Y , KANG Y , ZOU T Y , et al . Vertical federated learning: concepts, advances, and challenges [J ] . IEEE Transactions on Knowledge and Data Engineering , 2024 , 36 ( 7 ): 3615 - 3634 .

周传鑫 , 孙奕 , 汪德刚 , 等 . 联邦学习研究综述 [J ] . 网络与信息安全学报 , 2021 , 7 ( 5 ): 77 - 92 .

ZHOU C X , SUN Y , WANG D G , et al . Survey of federated learning research [J ] . Chinese Journal of Network and Information Security , 2021 , 7 ( 5 ): 77 - 92 .

王健宗 , 孔令炜 , 黄章成 , 等 . 联邦学习隐私保护研究进展 [J ] . 大数据 , 2021 , 7 ( 3 ): 130 - 149 .

WANG J Z , KONG L W , HUANG Z C , et al . Research advances on privacy protection of federated learning [J ] . Big Data Research , 2021 , 7 ( 3 ): 130 - 149 .

中国信息通信研究院 . 联邦学习场景应用研究报告 [R ] . 2022 .

China Academy of Information and Communications Technology (CAICT) . Research Report on Federated Learning Application Scenarios [R ] . 2022 .

闫树 , 吕艾临 . 隐私计算发展综述 [J ] . 信息通信技术与政策 , 2021 ( 6 ): 1 - 11 .

YAN S , LYU A L . Overview of the development of privacy preserving computing [J ] . Information and Communications Technology and Policy , 2021 ( 6 ): 1 - 11 .

王伟 , 邵瑜 , 段佳 , 等 . 隐私计算: 技术方法和行业应用的综述 [J ] . 中国计算机科学评论 , 2023 , 1 ( 1 ): 1 - 12 .

WANG W , SHAO Y , DUAN J , et al . Privacy-preserving computation: a comprehensive survey of methods and applications [J ] . China Computer Science Review , 2023 , 1 ( 1 ): 1 - 12 .

王鹏飞 , 魏宗正 , 周东生 , 等 . 联邦忘却学习研究综述 [J ] . 计算机学报 , 2024 , 47 ( 2 ): 396 - 422 .

WANG P F , WEI Z Z , ZHOU D S , et al . A survey on federated unlearning [J ] . Chinese Journal of Computers , 2024 , 47 ( 2 ): 396 - 422 .

高莹 , 陈晓峰 , 张一余 , 等 . 联邦学习系统攻击与防御技术研究综述 [J ] . 计算机学报 , 2023 , 46 ( 9 ): 1781 - 1805 .

GAO Y , CHEN X F , ZHANG Y Y , et al . A survey of attack and defense techniques for federated learning systems [J ] . Chinese Journal of Computers , 2023 , 46 ( 9 ): 1781 - 1805 .

王冬 , 秦倩倩 , 郭开天 , 等 . 联邦学习中的模型逆向攻防研究综述 [J ] . 通信学报 , 2023 , 44 ( 11 ): 94 - 109 .

WANG D , QIN Q Q , GUO K T , et al . Survey on model inversion attack and defense in federated learning [J ] . Journal on Communications , 2023 , 44 ( 11 ): 94 - 109 .

FREDRIKSON M , JHA S , RISTENPART T . Model inversion attacks that exploit confidence information and basic countermeasures [C ] // Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security . New York : ACM , 2015 : 1322 - 1333 .

张佳乐 , 朱诚诚 , 孙小兵 , 等 . 基于GAN的联邦学习成员推理攻击与防御方法 [J ] . 通信学报 , 2023 , 44 ( 5 ): 193 - 205 .

ZHANG J L , ZHU C C , SUN X B , et al . Membership inference attack and defense method in federated learning based on GAN [J ] . Journal on Communications , 2023 , 44 ( 5 ): 193 - 205 .

SHOKRI R , STRONATI M , SONG C , et al . Membership inference attacks against machine learning models [C ] // Proceedings of the 2017 IEEE Symposium on Security and Privacy . Piscataway : IEEE Press , 2017 : 3 - 18 .

朱建明 , 张沁楠 , 高胜 , 等 . 基于区块链的隐私保护可信联邦学习模型 [J ] . 计算机学报 , 2021 , 44 ( 12 ): 2464 - 2484 .

ZHU J M , ZHANG Q N , GAO S , et al . Privacy preserving and trustworthy federated learning model based on blockchain [J ] . Chinese Journal of Computers , 2021 , 44 ( 12 ): 2464 - 2484 .

韩益亮 , 宋超越 , 吴旭光 , 等 . 区块链与隐私计算融合技术综述 [J ] . 科学技术与工程 , 2024 , 24 ( 28 ): 11945 - 11963 .

HAN Y L , SONG C Y , WU X G , et al . Review of integrated technology of blockchain and privacy computing [J ] . Science Technology and Engineering , 2024 , 24 ( 28 ): 11945 - 11963 .

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