A Data Privacy Protection Scheme Based on Federated Learning

doi:10.3969/j.issn.1006-2475.2025.08.009

Abstract

Abstract:
Abstract: The current healthcare data domain faces the issue of data silos， which restricts the flow and sharing of data among different institutions and hinders cross-institutional treatment for patients. To address this problem， this paper proposes a privacy protection scheme based on federated learning （Federated Learning with Schnorr Zero-knowledge Based Identity Authentication and Differential Privacy Protection， FL-SZIDP）. Firstly， a data-sharing framework based on federated learning is established. Secondly， to defend against adversaries attempting to steal original data through reverse attacks， differential privacy noise is added to the model parameters uploaded by each participant. To prevent malicious participants from joining the federated learning process， identity authentication based on Schnorr zero-knowledge proof is performed， ensuring the credibility of the participants’ identities. Finally， the effectiveness of the proposed algorithm is verified using the MNIST data set. The experimental results show that the scheme FL-SZIDP ensures accuracy while protecting privacy.

Key words: Key words: federal learning, privacy protection, differential privacy, data security

CLC Number:

中图分类号：TP391

CHENG Yuwen, JING Yijun, SHI Zicheng, JING Changqiang, GUO Feng, WU Chuankun. A Data Privacy Protection Scheme Based on Federated Learning[J]. Computer and Modernization, 2025, 0(08): 63-69.

References

［1］ YIN L H， FENG J Y， XUN H， et al. A privacy-preserving federated learning for multiparty data sharing in social IoTs［J］. IEEE Transactions on Network Science and Engineering， 2021，8（3）:2706-2718.
［2］ N T R， GUPTA R. A survey on machine learning approaches and its techniques［C］// 2020 IEEE International Students’ Conference on Electrical， Electronics and Computer Science（SCEECS）. IEEE， 2020. DOI: 10.1109/SCEECS48394.
2020.190.
［3］ LI L， FAN Y X， TSE M， et al. A review of applications in federated learning［J］. Computers & Industrial Engineering， 2020，149. DOI: 10.1016/j.cie.2020.106854.
［4］ TRAMÈR F， ZHANG F， JUELS A， et al. Stealing machine learning models via prediction APIs［C］// Proceedings of the 25th USENIX on Security Symposium. USENIX Association， 2016:601- 618.
［5］ HOSSEINI S M， SIKAROUDI M， BABAEI M， et al. Cluster based secure multi-party computation in federated learning for histopathology images［C］// International Workshop on Distributed， Collaborative， and Federated Learning. Springer， 2022:110-118.
［6］金旋，姚远志，俞能海. 基于秘密共享的隐私保护联邦学习高效安全聚合方案（英文）［J］. 中国科学技术大学学报， 2024，54（1）:32-47.
［7］ WIBAWA F， CATAK F O， KUZLU M， et al. Homomorphic encryption and federated learning based privacy-preserving CNN training: Covid-19 detection use-case［C］// Proceedings of the 2022 European Interdisciplinary Cybersecurity Conference. ACM， 2022:85-90.
［8］ ZONG H X， WANG Q， LIU X F， et al. Communication reducing quantization for federated learning with local differential privacy mechanism［C］// 2021 IEEE/CIC International Conference on Communications in China （ICCC）. IEEE， 2021:75-80.
［9］ GUO L H， GAO W L， CAO Y， et al. Research on medical data security sharing scheme based on homomorphic encryption［J］. Mathematical Bioscience and Engineering， 2023，20（2）:2261-2279.
［10］ KALAPAAKING A P， STEPHANIE V， KHALIL I， et al. SMPC-based federated learning for 6G-enabled Internet of Medical Things［J］. IEEE Network， 2022，36（4）:182-189.
［11］ GEYER R C， KLEIN T， NABI M. Differentially private federated learning: A client level perspective［J］. arXiv preprint arXiv:1712.07557， 2017.
［12］王珊，荆桃，肖淦文，等. 联邦学习下高效的隐私保护安全聚合方案［J］. 计算机系统应用， 2023，32（11）:175-181.
［13］ LIU W， ZHANG Y H， LI Y F， et al. A fine-grained medical data sharing scheme based on federated learning［J］. Concurrency and Computation: Practice and Experience， 2023，35（20）. DOI: 10.1002/cpe.6847.
［14］杨帆，孙奕，陈性元，等. 基于同态代理重认证的可验证联邦聚合方法［J］. 网络与信息安全学报， 2024，10（3）:91-106.
［15］王腾，霍峥，黄亚鑫，等. 联邦学习中的隐私保护技术研究综述［J］. 计算机应用， 2023，43（2）:437-449.
［16］ KONEČNÝ J， MCMAHAN H B， RAMAGE D， et al. Federated optimization: Distributed machine learning for on-device intelligence［J］. arXiv preprint arXiv:1610.02527， 2016.
［17］熊世强，何道敬，王振东，等. 联邦学习及其安全与隐私保护研究综述［J］. 计算机工程， 2024，50（5）:1-15.
［18］ ZHANG X Z， MAVROMATIS A， VAFEAS A， et al. Federated feature selection for horizontal federated learning in IoT networks［J］. IEEE Internet of Things Journal， 2023，10（11）:10095-10112.
［19］ DWORK C， MCSHERRY F， NISSIM K， et al. Calibrating noise to sensitivity in private data analysis［C］// Proceedings of the 3rd Theory of Cryptography Conference （TCC 2006）. Springer， 2006:265-284.
［20］ WEI K， LI J， DING M， et al. Federated learning with differential privacy: Algorithms and performance analysis［J］. IEEE Transactions on Information Forensics and Security， 2020，15:3454-3469.
［21］ DWORK C. A firm foundation for private data analysis［J］. Communications of the ACM， 2011，54（1）:86-95.
［22］ ZHANG D Y， CHEN X J， SHI J Q. An efficient federated convolutional neural network scheme with differential privacy［C］// 3rd International Conference on Emerging Information Security and Applications. Springer， 2022:173-190.
［23］ JI S， ZHANG J L， ZHANG Y J， et al. LAFED: A lightweight authentication mechanism for blockchain-enabled federated learning system［J］. Future Generation Computer Systems， 2023，145:56-67.
［24］ ZHONG Y D， HOVANES J， GUIN U. On-demand device authentication using zero-knowledge proofs for smart systems［C］// Proceedings of the Great Lakes Symposium on VLSI 2023. ACM， 2023:569-574.
［25］张旭. 基于差分隐私机制的数据隐私保护的研究［D］. 南京:南京邮电大学， 2022.
［26］ MCMAHAN H B， MOORE E， RAMAGE D， et al. Communication-efficient learning of deep networks from decentralized data［C］// Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. PMLR， 2017，54:1273-1282.
［27］ MAJOR W， BUCHANAN W J， AHMAD J. An authentication protocol based on chaos and zero knowledge proof［J］. Nonlinear Dynamics， 2020，99（4）:3065-3087.
［28］ LI T， SAHU A K， ZAHEER M， et al. Federated optimization in heterogeneous networks［J］. Proceedings of Machine Learning and Systems， 2020，2:429-450.
［29］ ASAD M， MOUSTAFA A， ITO T. FedOpt: Towards communication efficiency and privacy preservation in federated learning［J］. Applied Sciences， 2020，10（8）. DOI: 10.3390/
app10082864.

[1]	XUE Wenxue, XIE Yingde. Privacy-preservation Models for Identifying Financial Fraud in Enterprises [J]. Computer and Modernization, 2025, 0(06): 16-20.
[2]	YUAN Hongwei1, CHANG Lijun1, HAO Jianhuan2, FAN Na2, WANG Chao2, LUO Chuang2, ZHANG Zehui2. Trajectory Interest Points Mining Based on Label Propagation and Privacy Protection [J]. Computer and Modernization, 2024, 0(05): 46-54.
[3]	YANG Ju, DENG Zhiliang, YANG Zhiqiang, WANG Yan, ZHAO Zhongyuan. Decentralized Federation Learning Based on Fed-DPDOBO [J]. Computer and Modernization, 2024, 0(04): 99-106.
[4]	AO Bochao, FAN Bingbing. Federated Learning Aggregation Algorithm Based on AP Clustering Algorithm [J]. Computer and Modernization, 2024, 0(04): 5-11.
[5]	ZHA Kai-jin, WANG Zhi-bo, HE Yue-shun, XU Hong-zhen. Survey on Blockchain Security Protection [J]. Computer and Modernization, 2023, 0(06): 110-117.
[6]	FENG Li-gang, ZHU You-wen, . Utility-optimized Local Differential Privacy Mechanism for Protecting Location Privacy [J]. Computer and Modernization, 2022, 0(09): 99-105.
[7]	ZHANG Tian-xi, WANG Li-peng, ZHOU Chun-tian, YANG Yan-yan, LI Xiao-chong. Secret Content Auditing Scheme in Internet of Things Scenes Based on Blockchain [J]. Computer and Modernization, 2022, 0(03): 30-36.
[8]	ZHAO Long, LONG Shi-gong, . Crowdsourcing Privacy Protection Method to Local Differential Privacy [J]. Computer and Modernization, 2021, 0(07): 115-119.
[9]	ZUO Peng, HE Zhi-mou, YUAN Meng, ZHANG Hai-kuo, YANG Wei-ping. Identifier Resolution Model Based on Encryption Transmission [J]. Computer and Modernization, 2020, 0(04): 46-.
[10]	SHANG Jing-wei1,2, JIANG Rong1,2, HU Xiao-han1,2, SHI Ming-yue1,2. Medical Big Data and Privacy Leakage [J]. Computer and Modernization, 2019, 0(07): 111-.
[11]	CHEN Yang， YU Shoujian. Research on Differential Privaty for Decision Tree Release Technology [J]. Computer and Modernization, 2017, 0(3): 59-.
[12]	LIU Bo, HUANG Zhi-qiu, WANG Shan-shan. A Method of Service Evolution for Controlling Risk of Privacy Exposure [J]. Computer and Modernization, 2014, 0(10): 76-80,107.
[13]	ZHANG Xing-lan;YU Jin-ying. Strategy for Privacy Protection of Supporting Data Modification [J]. Computer and Modernization, 2013, 1(8): 175-178,.
[14]	XU Shuai. Research on Enforcing Privacy Security in MapReduce Based on Decentralized Information Flow Control [J]. Computer and Modernization, 2012, 1(03): 93-97.
[15]	RAO Lan-xiang. Research on Data Mining Based on Privacy Protection [J]. Computer and Modernization, 2011, 12(12): 109-111.