期刊文献+

隐私计算在卫生健康行业的应用与安全研究 被引量:1

Application and security research of privacy computing in health industry
下载PDF
导出
摘要 医疗健康数据难以安全共享、开放与利用是阻碍当前卫生健康行业信息化发展的最重要因素之一。研究探讨了卫生健康行业在数据共享与利用的实际应用场景下面临的困境,提出了隐私计算在各应用场景下的应用方案,并根据实际需求给出了对应场景下隐私计算模型安全的建议。 Difficulty in safe sharing, openness and utilization of medical and health data is one of the most important factors hindering the development of informatization in the current health and health industry. This paper researches and discusses the difficulties faced by the health industry in the actual application scenarios of data sharing and utilization, proposes the application schemes of privacy computing in various application scenarios, and gives suggestions on the security of privacy computing models in corresponding scenarios according to actual needs.
作者 鞠鑫 曹京 陈佛忠 刘文懋 胡忠华 JU Xin;CAO Jing;CHEN Fozhong;LIU Wenmao;HU Zhonghua(Health and Family Planning Statistics Information Center of Suzhou,Suzhou 215002,China;Security Research Institute,China Academy of Information and Communications Technology,Beijing 100191,China;NSFOCUS Technologies,Inc.,Beijing 100089,China)
出处 《信息通信技术与政策》 2023年第2期43-48,共6页 Information and Communications Technology and Policy
关键词 隐私计算 数据安全 联邦学习 模型安全 可信执行环境 privacy computing data security federated learning model security trusted execution environment
  • 相关文献

参考文献5

二级参考文献95

  • 1CULNAN M J, ARMSTRONG P K. Information privacy concerns, procedural fairness, and impersonal trust: an empirical investigation[J]. Organization Science, 1999, 10(1): 104-115.
  • 2DINEV T, HART P. Privacy concems and intemet use-a model of trade-off factors[C]//,~cademy of Management. c2003:1-6.
  • 3LI H, SARATHY R, XU H. Understanding situational online informa- tion disclosure as a privacy calculus[J]. Journal of Computer Informa- tion Systems, 2010, 51(1): 62-71.
  • 4KEHR F, KOWATSCH T, WENTZEL D, et al. Blissfully ignorant: the effects of general privacy concerns, general institutional trust, and af- fect in the privacy calculus[J]. Information Systems Journal, 2015, 25(6): 607-635.
  • 5MACHANAVAJJHALA A, KIFER D, GEHRKE J, ~t al. /-diversity: privacy beyond k-anonymity[J]. ACM Transactions on Knowledge Discovery from Data (TKDD), 2007, 1(1): 3.
  • 6AGRAWAL D, AGGARWAL C C. On the design and quantification of privacy preserving data mining algorithms[C]//The 20th ACM SIG- MOD-SIGACT-SIGART Symposium on Principles of Database Sys- tems. ACM, c2001 : 247-255.
  • 7LIU K, KARGUPTA H, RYAN J. Random projection-based multipli- eative data perturbation for privacy preserving distributed data min- ing[J]. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(1): 92-106.
  • 8OLIVEIRA S R M, ZAIANE O R. Privacy preserving clustering by data transformation[C]//The 18th Brazilian Symposium on Databases. c2003:304-318.
  • 9OLIVEIRA S R M, ZAIANE O R. Privacy preserving clustering by object similarity-based representation and dimensionality reduction transformation[C]/Fl'he Workshop on Privacy and Security Aspects of Data Mining. e2004:21-30.
  • 10OLIVEIRA S R M, ZAIANE 0 R. Privacy preserving frequent itemsetmining[C]//The IEEE International Conference on Privacy, Security and Data Mining-Volume 14, Australian Computer Society. c2002: 43-54.

共引文献95

同被引文献10

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部