Analysis and suggestions on the standardization path of privacy computing technology

doi:10.12267/j.issn.2096-5931.2024.01.005

Abstract

Abstract:

In the context of national requirements for personal information protection and efficient flow of data elements, the privacy computing technology has attracted widespread attention as a key technology to solve data security and flow problems. Standardization of privacy computing technology has become an important research topic in the field of personal information protection. This article focuses on the analysis of the standardization path for privacy computing, and discusses the current situation, existing problems, and relevant suggestions of standardization, aiming to provide reference for the standardization construction of privacy computing technology.

Key words: privacy computing, standardization, personal information protection

CLC Number:

TP309

WANG Yanhong, KONG Ling, FU Yanyan, LI Jingdian. Analysis and suggestions on the standardization path of privacy computing technology[J]. Information and Communications Technology and Policy, 2024, 50(1): 32-36.

Add to citation manager EndNote|Ris|BibTeX

URL:

http://ictp.caict.ac.cn/EN/10.12267/j.issn.2096-5931.2024.01.005

http://ictp.caict.ac.cn/EN/Y2024/V50/I1/32

References 17

[1]	UNCTAD. Data protection and privacy legislation worldwide[EB/OL]. 2023[2023-12-01]. https://unctad.org/page/data-protection-and-privacy-legislation-worldwide.
[2]	李凤华, 李晖, 牛犇, 等. 隐私计算的学术内涵与研究趋势[J]. 网络与信息安全学报, 2022, 8(6):1-8.
[3]	李凤华, 李晖, 贾焰, 等. 隐私计算研究范畴及发展趋势[J]. 通信学报, 2016, 37(4):1-11. doi: 10.11959/j.issn.1000-436x.2016078
[4]	YANG Q, LIU Y, CHEN T, et al. Federated machine learning: concept and applications[J]. ACM Transactions on Intelligent Systems and Technology, 2019, 10(2):1-19. DOI:10.1145/3298981.
[5]	ZHANG C, XIE Y, BAI H, et al. A survey on federated learning[J]. Knowledge-Based Systems, 2021, 216(1):1065. DOI:10.1016/j.knosys.2021.1065.
[6]	DU W, ATALLAH M J. Secure multi-party computation problems and their applications: a review and open problems[C]// Proceedings of the 2001 Workshop on New Security Paradigms. New York: ACM, 2001:13-22.
[7]	SABT M, ACHEMLAL M, BOUABDALLAH A. Trusted execution environment: what it is, and what it is not[C]// 2015 IEEE Trustcom/BigDataSE/Ispa. Helsinki: IEEE Press, 2015, 1:57-64. DOI:10.1109/Trustcom.2015.357.
[8]	ZHENG W, WU Y, WU X X, et al. A survey of Intel SGX and its applications[J]. Frontiers of Computer Science, 2021, 15(3):1-15.
[9]	PINTO S, SANTOS N. Demystifying arm trustzone: a comprehensive survey[J]. ACM Computing Surveys, 2019, 51(6):1-36. DOI:10.1145/3291047.
[10]	ISO. Information security secure multiparty computation part 1: general:ISO 4922-1:2023[S], 2023:7.
[11]	ISO IEC. Information security secure multiparty computation part 2: mechanisms based on secret sharing: ISO/IEC 4922-2[S], Under Development.
[12]	IEEE. Guide forarchitectural framework and application of federated machine learning:IEEE 3652.1-2020[S], 2021:3.
[13]	ITU-T. Technical framework for the shared machine learning system: ITU-T F.748.13-2021[S], 2021:6.
[14]	国家市场监督管理总局, 国家标准化管理委员会. 信息安全技术个人信息去标识化指南: GB/T 37964—2019[S]. 北京: 中国标准出版社, 2019:8.
[15]	国家市场监督管理总局, 国家标准化管理委员会. 信息安全技术个人信息去标识化效果评估指南: GB/T 42460—2023[S]. 北京: 中国标准出版社, 2023:3.
[16]	中国通信标准化协会. 基于可信执行环境的安全计算系统技术框架: YD/T 4234—2023[S]. 北京: 人民邮电出版社, 2023:5.
[17]	电信终端产业协会. 基于差分隐私的用户个人信息保护技术要求: T/TAF 137—2022[S], 2022:11.