Application of Privacy Computing to Smart Grid Data Sharing Platform

被引:0
作者
Jia, Ning [1 ]
Gao, QiaoMei [1 ]
Liu, YongMei [1 ]
Meng, Hao [2 ]
Liu, ShuQing [3 ]
Yi, FengChao [3 ]
机构
[1] Inner Mongolia Univ, Coll Comp Sci, Hohhot, Peoples R China
[2] Inner Mongolia Univ, Finance Dept, Hohhot, Peoples R China
[3] Inner Mongolia IEDS Co Ltd, Tech Res Inst, Hohhot, Peoples R China
来源
2023 2ND ASIAN CONFERENCE ON FRONTIERS OF POWER AND ENERGY, ACFPE | 2023年
关键词
Data Sharing; Privacy Computing; State Secrets Algorithms; Data Elements; Smart Grids;
D O I
10.1109/ACFPE59335.2023.10455666
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Data, as an emerging factor of production, is profoundly changing the traditional mode of production and at the same time giving rise to the need for a data-centered digital economy. Currently, data sharing between governments and enterprises as well as between enterprises is being rapidly explored. This study focuses on the big data information sharing service platform of an electric power company, which mainly buttresses government departments such as the Discipline Inspection and Supervision Commission of the Autonomous Region, the Development and Reform Commission, and the Bureau of Energy, providing them with information such as user profiles, electricity consumption, meter readers, and payment status of the relevant personnel houses, as well as providing electric power data services for government agencies and other units. However, in the process of data sharing, the risks of low-cost data replication, data leakage, and personal privacy violation come to the fore. Therefore, how to ensure data availability while maintaining data security and privacy protection has become an urgent challenge for data sharing today. The platform uses privacy computing technology to solve the problem of sharing data elements, realizes the goal of "original data not out of domain" in the flow mode, ensures the "available and invisible" state of data in the process of sharing and fusion, realizes the transfer of data value only, breaks the information chimney between departments and regions, and reduces the risk of violation of personal privacy. It has broken the information chimney between departments and regions, and achieved real information sharing.
引用
收藏
页码:63 / 68
页数:6
相关论文
共 7 条
[1]  
CHEN F, 2021, Computers & Security, V103
[2]  
Fang P, 2021, P 11 INT C COMP EN 2, P3038, DOI [10.26914/c.cnkihy.2021.044855, DOI 10.26914/C.CNKIHY.2021.044855]
[3]   FEDERATED LEARNING WITH LOCAL DIFFERENTIAL PRIVACY: TRADE-OFFS BETWEEN PRIVACY, UTILITY, AND COMMUNICATION [J].
Kim, Muah ;
Guenlue, Onur ;
Schaefer, Rafael F. .
2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021), 2021, :2650-2654
[4]  
Krishnan S, 2023, Handbook on Federated Learning: Advances, Applications and Opportunities
[5]  
Louise T, 2022, Computers & Security
[6]  
Tu A T., 2021, Neurocomputing, P422
[7]  
Wang Z, 2019, P 9 INT C COMP ENG N, DOI [10.26914/c.cnkihy.2019.048135, DOI 10.26914/C.CNKIHY.2019.048135]