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文章摘要
基于隐私同态的城市配电网多级网格数据聚合算法
Multi-level grid data aggregation algorithm for urban distribution network based on privacy homomorphism
Received:January 07, 2022  Revised:February 14, 2022
DOI:10.19753/j.issn1001-1390.2002.09.015
中文关键词: 隐私同态  配电网  多级网格数据  聚合  密度阈值函数  聚类中心  
英文关键词: privacy  homomorphism, distribution  network, multi-level  grid data, aggregation, density  threshold function, clustering  center
基金项目:中国南方电网科技项目(670000KK58200011)
Author NameAffiliationE-mail
Xiawei* China Southern Power Grid Digital Grid Research Institute Co.,Ltd. summerhi75@163.com 
Caiwenting China Southern Power Grid Digital Grid Research Institute Co.,Ltd. summerhi75@163.com 
Liuyang China Southern Power Grid Digital Grid Research Institute Co.,Ltd. summerhi75@163.com 
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中文摘要:
      针对城市配电网多级网格数据聚合过程中出现的数据隐私安全问题,提出了基于隐私同态的城市配电网多级网格数据聚合算法。首先,通过数据格式转化方法重新获取新的数据,并保证数据获取的完整性。其次,通过密度阈值函数设置、初始聚类中心选取与网格聚类等过程实现数据聚合,在此基础上,采用隐私同态技术加密聚合后的数据,实现基于隐私同态的城市配电网多级网格数据聚合。实验结果表明,所提出的基于隐私同态的城市配电网多级网格数据聚合算法,能够有效减少聚合后数据丢失与被篡改的次数,提高数据加密效率与聚合效率,降低数据的通信费用。
英文摘要:
      Due to the data privacy security problem within the process of data aggregation of multi-level grids in urban distribution network, a multi-level grid data aggregation algorithm of urban distribution network based on privacy homomorphism is proposed in the paper.Firstly, new data is re-acquired through the data format conversion method, and the integrity of data acquisition is ensured. Secondly, data aggregation is realized through the process of density threshold function setting, initial cluster center selection and grid clustering.On this basis, the privacy homomorphism technology is used to encrypt the aggregated data to realize the multi-level grid data aggregation of urban distribution network based on privacy homomorphism.The experimental results show that the proposed multi-level grid data aggregation algorithm for urban distribution network based on privacy homomorphism can effectively reduce the number of data loss and tampering after aggregation, improve data encryption efficiency and aggregation efficiency, and reduce data communication costs.
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