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[1]鞠鑫王怡顾奇.隐私计算在卫生健康行业数据要素安全流通中的应用探索[J].中国卫生质量管理,2026,33(5):12-16.[doi:10.13912/j.cnki.chqm.2026.33.5.03]
　JU Xin,WANG Yi,GU Qi.Exploration of Privacy Computing Applications in the Secure Circulation of Data Elements within the Health Industry[J].Chinese Health Quality Management,2026,33(5):12-16.[doi:10.13912/j.cnki.chqm.2026.33.5.03]

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隐私计算在卫生健康行业数据要素安全流通中的应用探索

《中国卫生质量管理》[ISSN:1006-7515/CN:CN 61-1283/R] 卷: 33 期数: 2026年5期页码: 12-16 栏目: 特别关注出版日期: 2026-05-15

Title:: Exploration of Privacy Computing Applications in the Secure Circulation of Data Elements within the Health Industry

作者:: 鞠鑫王怡顾奇; 苏州市卫生健康信息中心

Author(s):: JU Xin; WANG Yi; GU Qi; Suzhou Health Information Center

关键词:: 隐私计算; 卫生健康; 数据要素

Keywords:: Privacy Computing; Health Sector; Data Elements

分类号:: R197.3

DOI:: 10.13912/j.cnki.chqm.2026.33.5.03

文献标志码:: B

摘要:: 数据是一种关键要素，对其的开发离不开数据的共享流通与计算应用，这对数据安全提出了更高要求。隐私计算作为保障数据安全流通的核心技术，因其能够在协同计算中兼顾数据的可用性与不可见性，逐渐成为数据要素开发中的关键一环。本研究以苏州市卫生健康领域在隐私计算方面的探索应用为基础，总结了3类安全流通范式：联合科研、安全服务与离线计量，展示了隐私计算在跨机构数据协作、隐私数据保护和数据流通管理中的具体应用成效，为其他地区与行业的数据要素安全应用提供参考。

Abstract:: Data is a critical element, and its development relies on data sharing, circulation, and computational applications, which impose higher demands on data security. As a core technology ensuring the secure circulation of data, privacy computing has gradually become a key component in the development of data elements due to its ability to balance data availability and invisibility during collaborative computing. Based on the exploration and application of privacy computing in the health sector of Suzhou, this study summarizes three types of secure circulation paradigms: joint scientific research, secure services, and offline measurement. It demonstrates the specific application effectiveness of privacy computing in cross-institutional data collaboration, privacy data protection, and data circulation management, providing a reference for data element secure applications in other regions and industries.

参考文献/References:

［1］吴江,陶成煦. 激活数据要素赋能千行万业——《“数据要素×”三年行动计划(2024-2026年)》政策解读［J］. 情报理论与实践,2024,47(3):16-19.［2］陆家发,赵群,唐凯,等.江苏省全民健康信息平台数据治理实践研究［J］.中国卫生质量管理,2023,30(8):63-67.［3］曹晓均,翟志斌,张文峰.医院科研数据平台安全体系的构建与应用［J］.中国卫生质量管理,2024,31(9):82-86.［4］李少波,杨磊,李传江,等.联邦学习概述：技术、应用及未来［J］.计算机集成制造系统,2022,28(7):2119-2138.［5］蒋凯元.多方安全计算研究综述［J］.信息安全研究,2021,7(12):1161-1165.［6］ANTONIO M, RUBEN R, RODRIGO R, et al. A survey on the (in) security of trusted execution environments［J］. Comput Secur, 2023, 129: 103180.［7］EL OUADRHIRI A, ABDELHADI A. Differential privacy for deep and federated learning: a survey［J］. IEEE Access, 2022, 10: 22359-22380.［8］张锋巍,周雷,张一鸣,等.可信执行环境：现状与展望［J］.计算机研究与发展,2024,61(1):243-260.［9］HALDERMAN AJ, SCHOEN DS, HENINGER N, et al. Lest we remember: cold-boot attacks on encryption keys［J］. Commun ACM, 2009, 52(5): 91-98.［10］LIU Y,CHEN L,SUN G,et al.Association between genetically determined serum corin and the risk of stroke in chinese adults: a mendelian randomization study［J］. J Am Heart Assoc, 2024, 13(18): e035858.［11］国家病原微生物数据库.单向隐私计算［EB/OL］.(2023-05-20)［2026-02-12］.https://nmdc.cn/gcpathogen/privacy.［12］CONG K, MORENO RC,GAMA MBD, et al. Labeled PSI from homomorphic encryption with reduced computation and communication［G］.Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security,Republic of Korea,2021.［13］ABADI M, CHU A, GOODFELLOW I, et al. Deep learning with differential privacy［G］.Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security,Vienna,2016.

更新日期/Last Update: 2026-05-15