Inferring Personal Information from Demand-Response Systems

Current and upcoming demand-response systems provide increasingly detailed power-consumption data to utilities and a growing array of players angling to assist consumers in understanding and managing their energy use. The granularity of this data, as well as new players´ entry into the energy market, creates new privacy concerns. The detailed per-household consumption data that advanced metering systems generate reveals information about in-home activities that such players can mine and combine with other readily available information to discover more about occupants´ activities. The authors explore the technological aspects of this claim, focusing on the ways in which personally identifying information can be collected and repurposed. Their results show that, even with relatively unsophisticated hardware and data-extraction algorithms, some information about occupant behavior can be estimated with a high degree of accuracy. The authors propose a disclosure metric to aid in quantifying the impact of data collection on in-home privacy and construct an example metric for their experiment.