科研成果详情

题名Achieving differential privacy of data disclosure from non-intrusive load monitoring in smart grid
作者
发表日期2017
会议名称9th International Symposium on Cyberspace Safety and Security, CSS 2017
会议录名称Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN0302-9743
卷号10581 LNCS
页码32-42
会议日期OCT 23-25, 2017
会议地点Xi'an, CHINA
摘要

In smart grid, large quantities of smart meters are installed in customers’ homes to collect electricity usage data, which can then be used to draw the load curve versus time of a day, and develop a plan or model for power generation. However, such data can also reveal customer’s daily activities. In addition, a non-intrusive load monitoring (NILM) device can monitor an electrical circuit that contains a number of appliances which switch on and off independently. If an adversary analyzes the meter readings together with the data measured by NILM device, the customer’s privacy will be disclosed. In this paper, we propose an effective privacy-preserving scheme for electric load monitoring, which can guarantee differential privacy of data disclosure in smart grid. In the proposed scheme, an energy consumption behavior model based on Factorial Hidden Markov Model (FHMM) is established. In addition, Laplace noise is added to the behavior parameter, which is different from the traditional methods that usually add noise to the energy consumption data. The analysis shows that the proposed scheme can get a better trade-off between utility and privacy compared with other popular methods.

关键词Differential privacy Factorial Hidden Markov Model Kullback–Leibler divergence Non-intrusive load monitoring Smart grid
DOI10.1007/978-3-319-69471-9_3
URL查看来源
语种英语English
Scopus入藏号2-s2.0-85034245430
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被引频次[WOS]:0   [WOS记录]     [WOS相关记录]
文献类型会议论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/7245
专题个人在本单位外知识产出
通讯作者Cao, Hui
作者单位
1.School of Computer, Wuhan University, Wuhan, 430072, China
2.School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, China
3.North China Branch of State Grid Corporation of China, Beijing, 10053, China
4.Department of Mathematics and Computer Science, Fayetteville State University, Fayetteville, 28301, United States
5.College of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China
推荐引用方式
GB/T 7714
Cao, Hui,Liu, Shubo,Guan, Zhitaoet al. Achieving differential privacy of data disclosure from non-intrusive load monitoring in smart grid[C], 2017: 32-42.
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