Preventing Occupancy Detection from Smart Meters
Utilities are rapidly deploying smart meters that measure electricity usage in real-time. Unfortunately, smart meters indirectly leak sensitive information about a home’s occupancy, which is easy to detect because it highly correlates with simple statistical metrics, such as power’s mean, variance and range. To prevent occupancy detection, we propose using the thermal energy storage of electric water heaters already present in many homes. In essence, our approach, which we call combined heat and privacy (CHPr), modulates a water heater’s power usage to make it look like someone is always home. We design a CHPr-enabled water heater that regulates its energy usage to thwart a variety of occupancy detection attacks without violating its objective to provide hot water on demand and evaluate it in simulation using real data. Our results show that a standard 50-gal CHPr-enabled water heater prevents a wide range of state-of-the-art occupancy detection attacks.
Weatherman: Exposing Weather-Based Privacy Threats in Big Energy Data
Smart energy meters record electricity consumption and generation at fine-grained intervals and are among the most widely deployed sensors in the world. Energy data embeds detailed information about a building’s energy-efficiency, as well as the behavior of its occupants, which academia and industry are actively working to extract. In many cases, either inadvertently or by design, these third-parties only have access to anonymous energy data without an associated location. We present Weatherman, which leverages a suite of analytics techniques to localize the source of anonymous energy data. Our key insight is that energy consumption data, as well as wind and solar generation data, largely correlates with weather, e.g., temperature, wind speed, and cloud cover and that every location on Earth has a distinct weather signature that uniquely identifies it. Weatherman represents a serious privacy threat, but also a potentially useful tool for researchers working with anonymous smart meter data. We evaluate Weatherman’s potential in both areas by localizing data from over one hundred smart meters using a weather database that includes data from over 35,000 locations.
SunSpot: Exposing the Location of Anonymous Solar-Powered Homes
Homeowners are increasingly deploying grid-tied solar systems due to the rapid decline in solar module prices. The energy produced by these solar-powered homes is monitored by utilities and third parties using networked energy meters, which record and transmit energy data at fine-grained intervals. Such energy data is considered anonymous if it is not associated with identifying account information, e.g., a name and address. Thus, energy data from these “anonymous” homes are often not handled securely: it is routinely transmitted over the Internet in plaintext, stored unencrypted in the cloud, shared with third-party energy analytics companies, and even made publicly available over the Internet. Extensive prior work has shown that energy consumption data is vulnerable to multiple attacks, which analyze it to reveal a range of sensitive private information about occupant activities. However, these attacks are useless without knowledge of a home’s location. Our key insight is that solar energy data is not anonymous: since every location on Earth has a unique solar signature, it embeds detailed location information. To explore the severity and extent of this privacy threat, we design SunSpot to localize “anonymous” solar-powered homes using their solar energy data. We evaluate SunSpot on publicly available energy data from 14 homes with rooftop solar. We find that SunSpot can localize a solar-powered home to a small region of interest that is near the smallest possible area given the energy data resolution, e.g., within a ∼500m and ∼28km radius for per-second and per-minute resolution, respectively. SunSpot then identifies solar-powered homes within this region using crowd-sourced image processing of satellite data before applying additional filters to identify a specific home.