Privacy-preserving fine-grained data collection and processing
Smart metering systems have been widely adopted in many countries, i.e., the European Commission requires an 80% AMI (Advanced Metering Infrastructure) coverage by 2020. However, recent research indicates that privacy-sensitive information, such as residents’ actions and their presence/absence, can be revealed by analyzing smart meter recordings without a paramount effort. Such a privacy disclosure violates the safety and security laws in many countries – the deployment of smart meters in the Netherlands was even canceled by the parliament. Nevertheless, smart meter deployment is well under way in other European countries, the US, Canada, and Asia due to the significant power system efficiency improvement incurred. Therefore, protecting consumer privacy in AMI has attracted much attention in recent years.
The smart grid research team led by Prof. Xiuzhen Cheng at GW aims at designing an AMI architecture that can provide privacy-preserving fine-grained data collection and processing. The design faces a number of challenges such as the traceability of the smart meter data via side-channel information, the low computational capability of smart meters, and the real-time access requirement of smart meter data. This project is a component of a large project sponsored by the National Science Foundation under the CyberSEES program, which involves three collaborative institutions: University of Minnesota – Twin Cities, University of Michigan – Ann Arbor, and The George Washington University. The GW CyberSEES project is centered on computational methods for enhancing the environmental, economic, and social sustainability of next-generation power distribution networks.
Prof. Xiuzhen Cheng
Project sponsor: National Science Foundation