基于分布式差分隐私的多智能电表数据加密方案

IF 7.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Renwu Yan;Yang Zheng;Ning Yu;Cen Liang
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引用次数: 0

摘要

在智能电网快速发展的大趋势下,数据安全和隐私保护面临严峻挑战,在获取用户聚合数据的前提下保护单个用户的隐私数据受到广泛关注。本研究针对多个智能电表数据聚合时用户隐私泄露的问题,提出了一种基于差分隐私的加密方案。首先,我们使用改进的同态加密方法实现用户数据的加密聚合。其次,我们提出了一种双盲噪声添加协议,通过用户与云平台之间的交互产生分布式噪声,防止半诚信参与者通过相互勾结窃取数据。最后,仿真结果表明,在满足差分隐私机制的前提下,所提出的方案可以对多智能仪表数据的传输进行加密。即使攻击者拥有足够的背景知识,也能确保彼此的用电信息安全。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Smart Meter Data Encryption Scheme Based on Distributed Differential Privacy
Under the general trend of the rapid development of smart grids, data security and privacy are facing serious challenges; protecting the privacy data of single users under the premise of obtaining user-aggregated data has attracted widespread attention. In this study, we propose an encryption scheme on the basis of differential privacy for the problem of user privacy leakage when aggregating data from multiple smart meters. First, we use an improved homomorphic encryption method to realize the encryption aggregation of users' data. Second, we propose a double-blind noise addition protocol to generate distributed noise through interaction between users and a cloud platform to prevent semi-honest participants from stealing data by colluding with one another. Finally, the simulation results show that the proposed scheme can encrypt the transmission of multi-intelligent meter data under the premise of satisfying the differential privacy mechanism. Even if an attacker has enough background knowledge, the security of the electricity information of one another can be ensured.
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来源期刊
Big Data Mining and Analytics
Big Data Mining and Analytics Computer Science-Computer Science Applications
CiteScore
20.90
自引率
2.20%
发文量
84
期刊介绍: Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge. Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications. Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more. With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.
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