Inferring Personal Information from Demand-Response Systems
IEEE Security and Privacy
Occupancy based demand response HVAC control strategy
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
A multi-sensor based occupancy estimation model for supporting demand driven HVAC operations
Proceedings of the 2012 Symposium on Simulation for Architecture and Urban Design
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Intuitively, measurements from utility meters that are associated with a physical space have embedded in them some information about the occupants of that space. Occupancy information can be sensitive yet empowering. On one hand, with the right information, administrators can adjust subsystems to maximize comfort and energy efficiency. On the other hand, sensitive details about occupants may be leaked. We explore the accuracy to which meter data from physical spaces, when subjected to machine learning algorithms, can yield occupancy information. Our results can then be used to devise low-cost mechanisms for occupancy sensing from the opportunistic use of already available data, and to quantify the risk of leaking privacy-sensitive inferences.