Protecting consumer privacy from electric load monitoring

Abstract

The smart grid introduces concerns for the loss of consumer privacy; recently deployed smart meters retain and distribute highly accurate profiles of home energy use. These profiles can be mined by Non Intrusive Load Monitors (NILMs) to expose much of the human activity within the served site. This paper introduces a new class of algorithms and systems, called Non-Intrusive Load Leveling (NILL) to combat potential invasions of privacy. NILL uses an in-residence battery to mask variance in load on the grid, thus eliminating exposure of the appliance-driven information used to compromise consumer privacy. We use real residential energy use profiles to drive four simulated deployments of NILL. The simulations show that NILL exposes only 1.1 to 5.9 useful energy events per day hidden amongst hundreds or thousands of similar batterysuppressed events. Thus, the energy profiles exhibited by NILL are largely useless for current NILM algorithms. Surprisingly, such privacy gains can be achieved using battery systems whose storage capacity is far lower than the residence's aggregate load average. We conclude by discussing how the costs of NILL can be offset by energy savings under tiered energy schedules.

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.…”

Privacy Preserving Similarity Detection for Data Analysis

“…In such a scenario the user that has a good guess for the coefficient of another user can reveal only the coefficients that are involved in a common random scaling factor per vector. That is if U i has 5 coefficients and it defines cosine similarity in between the ((1,2), (3,4), (1,5)) coefficients then an adversary with a good guess for the first coefficient can recover only the second and fifth coefficient and nothing more, since for the second subvector the user would choose different a random scaling factor.…”

“…Since the input to the data analysis operations is personal sensitive private information and operations performed over them violate user privacy. As such, users and companies either tend not to submit their data for further analysis to untrusted parties or they give limited access on it due to individual privacy violation risks [1,2,3,4]. Radical solutions include a restriction either on the available data analysis operations from the analyzer perspective or an outsource of aggregate information instead of individual data.…”

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.…”