Inferring Occupancy from Opportunistically Available Sensor Data


Commercial and residential buildings are usually instrumented with meters and sensors that are deployed as part of a utility infrastructure installed by companies that provide services such as electricity, water, gas, security, phone, etc. As part of their normal operation, these service providers have direct access to information from the sensors and meters. A concern arises that the sensory information collected by the providers, although coarse-grained, can be subject to analysis that reveals private information about the users of the building. Oftentimes, multiple services are provided by the same company, in which case the potential for leakage of private information increases. Our research seeks to investigate the extent to which easily available sensory information may be used by external service providers to make occupancy-related inferences. Particularly, we focus on inferences from two different sources: motion sensors, which are installed and monitored by security companies, and smart electric meters, which are deployed by electric companies for billing and demand-response management. We explore the motion sensor scenario in a three-person single-family home and the electric meter scenario in a twelve-person university lab. Our exploration with various inference methods shows that sensory information available to service providers can enable them to make undesired occupancy related inferences, such as levels of occupancy or even the identities of current occupants, significantly better than naive prediction strategies that do not make use of sensor information.

In IEEE International Conference on Pervasive Computing and Communications (Percom’14), 2014