Minimizing private data disclosures in the smart grid

Abstract

Smart electric meters pose a substantial threat to the privacy of individuals in their own homes. Combined with non-intrusive load monitors, smart meter data can reveal precise home appliance usage information. An emerging solution to behavior leakage in smart meter measurement data is the use of battery-based load hiding. In this approach, a battery is used to store and supply power to home devices at strategic times to hide appliance loads from smart meters. A few such battery control algorithms have already been studied in the literature, but none have been evaluated from an adversarial point of view. In this paper, we first consider two well known battery privacy algorithms, Best Effort (BE) and Non-Intrusive Load Leveling (NILL), and demonstrate attacks that recover precise load change information, which can be used to recover appliance behavior information, under both algorithms. We then introduce a stepping approach to battery privacy algorithms that fundamentally differs from previous approaches by maximizing the error between the load demanded by a home and the external load seen by a smart meter. By design, precise load change recovery attacks are impossible. We also propose mutual-information based measurements to evaluate the privacy of different algorithms. We implement and evaluate four novel algorithms using the stepping approach, and show that under the mutual-information metrics they outperform BE and NILL.

Scaling distributed energy storage for grid peak reduction

“…Unfortunately, controllers capable of programmatically setting the rate of discharge are not widely available, since their primary purpose today is in testing equipment [26]. However, programmatic control may become more widespread in the future, since recent work beyond our own also requires this capability [14,25]. We assume this latter method is available to control the discharge rate in PeakCharge.…”

“…Consumers have little monetary incentive to use these techniques today, since most deferrable loads, e.g., refrigerators, air conditioners, heaters, dehumidifiers, are unable to defer their usage (by up to 12 hours) to low-price nighttime periods without causing significant harm, e.g., spoiled food or an uncomfortable environment. In addition, as recent work shows, flattening demand using a battery preserves privacy [14,25], since it removes power variations that Non-Intrusive Load Monitoring (NILM) algorithms use to identify appliance usage and behavioral patterns. Unfortunately, with existing variable rate plans consumers with a battery must choose to either use it to reduce their electricity bill or preserve privacy, but not both.…”

Performance Analysis of Secure and Private Billing Protocols for Smart Metering

“…The approach in [7] also achieves differential privacy and should be economical for houses already using energy storage systems. Other works using rechargeable batteries include [8][9][10]. Energy forecasting may still be improved under these noise-based systems because the utility consumption after noise adjustment can be safely studied by utility providers.…”

Enhancing user privacy by preprocessing distributed smart meter data

“…Finally, instead of manipulating the collected readings, external storage components have been discussed as options to alter a building's consumption and thus eliminate characteristic features from the data. The use of batteries to smooth the load curve has been presented in [20], [21], but the limitations of state-of-the-art battery technology, e.g., decreasing capacities and high financial cost, render this technology inapplicable for many scenarios.…”