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)
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ISSN | 0302-9743 |
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 |
DOI | 10.1007/978-3-319-69471-9_3 |
URL | 查看来源 |
语种 | 英语English |
Scopus入藏号 | 2-s2.0-85034245430 |
引用统计 | |
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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