2014 IEEE International Conference on Pervasive Computing and Communications (PerCom) (2014)
March 24, 2014 to March 28, 2014
Dong Chen , University of Massachusetts Amherst, USA
David Irwin , University of Massachusetts Amherst, USA
Prashant Shenoy , University of Massachusetts Amherst, USA
Jeannie Albrecht , Williams College, USA
Electric utilities are rapidly deploying smart meters that record and transmit electricity usage in real-time. As prior research shows, smart meter data indirectly leaks sensitive, and potentially valuable, information about a home's activities. An important example of the sensitive information smart meters reveal is occupancy—whether or not someone is home and when. As prior work also shows, occupancy is surprisingly easy to detect, since it highly correlates with simple statistical metrics, such as power's mean, variance, and range. Unfortunately, prior research that uses chemical energy storage, e.g., batteries, to prevent appliance power signature detection is prohibitively expensive when applied to occupancy detection. To address this problem, we propose preventing occupancy detection using the thermal energy storage of large elastic heating loads already present in many homes, such as electric water and space heaters. In essence, our approach, which we call Combined Heat and Privacy (CHPr), controls the power usage of these large loads to make it look like someone is always home. We design a CHPr-enabled water heater that regulates its energy usage to mask occupancy without violating its objective, e.g., to provide hot water on demand, and evaluate it in simulation and using a prototype. Our results show that a 50-gallon CHPr-enabled water heater decreases the Matthews Correlation Coefficient (a standard measure of a binary classifier's performance) of a threshold-based occupancy detection attack in a representative home by 10x (from 0.44 to 0.045), effectively preventing occupancy detection at no extra cost.
Dong Chen, D. Irwin, P. Shenoy and J. Albrecht, "Combined heat and privacy: Preventing occupancy detection from smart meters," 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)(PERCOM), Budapest, Hungary, 2014, pp. 208-215.