Paper Info
Title
A New Privacy-Preserving Framework Based On Edge-Fog-Cloud Continuum For Load Forecasting
Abstract
As an essential part to intelligently fine-grained scheduling, planning and maintenance in smart grid and energy internet, short-term load forecasting makes great progress recently owing to the big data collected from smart meters and the leap forward in machine learning technologies. However, the centralized computing topology of classical electric information system, where individual electricity consumption data are frequently transmitted to the cloud center for load forecasting, tends to violate electric consumers' privacy as well as to increase the pressure on network bandwidth. To tackle the tricky issues, we propose a privacy-preserving framework based on the edge-fog-cloud continuum for smart grid. Specifically, 1) we gravitate the training of load forecasting models and forecasting workloads to distributed smart meters so that consumers' raw data are handled locally, and only the forecasting outputs that have been protected are reported to the cloud center via fog nodes; 2) we protect the local forecasting models that imply electricity features from model extraction attacks by model randomization; 3) we exploit a shuffle scheme among smart meters to protect the data ownership privacy, and utilize a re-encryption scheme to guarantee the forecasting data privacy. Finally, through comprehensive simulation and analysis, we validate our proposed privacy-preserving framework in terms of privacy protection, and computation and communication efficiency.
Year
DOI
Venue
2020
10.1109/WCNC45663.2020.9120680
2020 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC)
DocType
ISSN
Citations 
Conference
1525-3511
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Shiming Hou100.34
Hongjia Li242.81
Chang Yang3163.81
Liming Wang437.17