New technologies empower secure circulation of data elements

doi:10.12267/j.issn.2096-5931.2024.08.008

Abstract

Abstract:

Given that data elements will become an important driving force for the national economy, data element security has become a new trend in data security. Therefore, it is necessary to study the new technologies adopted in the process of data element security circulation, explain the differences between data element security and traditional data security, analyze new technologies such as privacy computing and confidential computing that can empower data elementsecurity, and analyze how to use new technologies to promote the secure circulation of data elements through scenario analysis of industry practice cases.

Key words: data elements, privacy computing, trusted execution environment

CLC Number:

F49
P309

LIU Wenmao, MENG Nan, TANG Xu. New technologies empower secure circulation of data elements[J]. Information and Communications Technology and Policy, 2024, 50(8): 55-61.

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URL:

http://ictp.caict.ac.cn/EN/10.12267/j.issn.2096-5931.2024.08.008

http://ictp.caict.ac.cn/EN/Y2024/V50/I8/55

Figures/Tables 8

References 8

[1]	徐攀, 李杰义. 企业数据资产入表路径: 框架与实践[J]. 财会月刊, 2024, 45(7):58-62.
[2]	KAIROUZ P, MCMAHAN H,. BAVENT B, et al. Advances and open problems in federated learning[J]. Foundations and Trends in Machine Learning. 2021, 14 (1-2): 1-210.
[3]	TOLPEGIN V, TRUEX S, GURSOY M E, et al. Data poisoning attacks against federated learningsystems[C]// Computer Security-ESORICS 2020: 25th European Symposium on Research in Computer Security. Guildford: Springer International Publishing, 2020: 480-501.
[4]	ZHANG J, ZHANG J, CHEN J, et al. Gan enhanced membership inference: a passive local attack in federated learning[C]// ICC 2020-2020 IEEE International Conference on Communications. Dublin: IEEE Press, 2020: 1-6.
[5]	LI J C, LI N H, RIBEIRO B. Effective passive membership inference attacks in federated learning against overparameterized models[EB/OL]. 2023[2024-07-11]. https://openreview.net/forum?id=QsCSLPP55Ku.
[6]	GOLDREICH O. Secure multi-party computation[J]. Manuscript. Preliminary Version, 1998, 78(110): 1-108.
[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: 57-64.
[8]	NIKOLENKO, SERGEY I. Synthetic data for deep learning[M]. Springer Nature, 2021.

比较维度	数据安全	数据要素安全
安全目的	重要数据不外泄	数据在流转过程可控
威胁模型	恶意攻击者	不诚实第三方
设计思维	逆向思维	正向思维
安全体系	面向攻防的安全体系(PPDR、CSF等)	可证明的安全体系
支撑技术	传统网络安全、数据安全和身份安全等	以密码学为基石的各类隐私计算、可信计算、区块链和机密计算技术
安全层级	安全底座	业务安全

比较维度	数据安全	数据要素安全
安全目的	重要数据不外泄	数据在流转过程可控
威胁模型	恶意攻击者	不诚实第三方
设计思维	逆向思维	正向思维
安全体系	面向攻防的安全体系(PPDR、CSF等)	可证明的安全体系
支撑技术	传统网络安全、数据安全和身份安全等	以密码学为基石的各类隐私计算、可信计算、区块链和机密计算技术
安全层级	安全底座	业务安全

[1]	LI XiaoLong, ZHANG Su, LYU Ailin. Research on mechanism and countermeasures of accelerating the process of industrial digitalization driven by data elements [J]. Information and Communications Technology and Policy, 2024, 50(4): 79-84.
[2]	GE Sifan, HAN Jianyang, DU Chunxiang, WANG Juntao. Research and practice on the construction of data element authorization operation platform for telecom operators [J]. Information and Communications Technology and Policy, 2024, 50(4): 85-90.
[3]	SHANG Runan, CHEN Wei, SUN Wei, XIE Xining. Research on telecom operators’ data element authorization mechanism [J]. Information and Communications Technology and Policy, 2024, 50(4): 91-96.
[4]	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.
[5]	JU Xin, CAO Jing, CHEN Fozhong, LIU Wenmao, HU Zhonghua. Application and security research of privacy computing in health industry [J]. Information and Communications Technology and Policy, 2023, 49(2): 43-48.
[6]	LIU Naijia, GUO Jian, LI Haihua. Research on application of privacy computing in distributed cognitive Industrial Internet [J]. Information and Communications Technology and Policy, 2023, 49(11): 25-32.
[7]	WANG Wubing, LIU Bo, FAN Yuan, TAO Lifeng, XU Dongde. A design and security analysis of the confidential computing architecture based on trusted execution environment [J]. Information and Communications Technology and Policy, 2022, 48(8): 8-18.
[8]	WEI Tao, PAN Wuqiong, LI Tingting, WEI Zhenqiang. Trusted-environment-based privacy preserving computing: breaks the bottleneck of ciphertext-exchange era [J]. Information and Communications Technology and Policy, 2022, 48(5): 15-24.
[9]	CHENG Rufan, WANG Lin, GUO Lanting, ZHENG Hao, SUN Qi, LI Zhi, WANG Shuang. Applications of privacy preserving computing in biomedical areas [J]. Information and Communications Technology and Policy, 2022, 48(5): 60-67.
[10]	HUANG Dengxi, PAN Xuefang. Research and practice of data asset value calculation [J]. Information and Communications Technology and Policy, 2022, 48(2): 29-36.
[11]	LEI Lei. Research on the current situation and development trend of data security [J]. Information and Communications Technology and Policy, 2022, 48(10): 69-74.
[12]	Yuxia SHI. Data element market situation, problems and suggestions [J]. Information and Communications Technology and Policy, 2022, 48(1): 19-23.
[13]	YUAN Bo, WANG Siyuan. Privacy preserving computing product evaluation system [J]. Information and Communications Technology and Policy, 2021, 47(6): 12-18.
[14]	SUN Ke. Problems and reflections on the development of value of data elements [J]. Information and Communications Technology and Policy, 2021, 47(6): 63-67.
[15]	HE Wei. Overview of China's digital economy development [J]. Information and Communications Technology and Policy, 2021, 47(2): 1-7.