Protecting consumer privacy from electric load monitoring

Stephen McLaughlin, Patrick McDaniel, William Aiello

Research output: Chapter in Book/Report/Conference proceedingConference contribution

166 Scopus citations

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 battery-suppressed 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.

Original languageEnglish (US)
Title of host publicationCCS'11 - Proceedings of the 18th ACM Conference on Computer and Communications Security
Pages87-98
Number of pages12
DOIs
StatePublished - 2011
Event18th ACM Conference on Computer and Communications Security, CCS'11 - Chicago, IL, United States
Duration: Oct 17 2011Oct 21 2011

Publication series

NameProceedings of the ACM Conference on Computer and Communications Security
ISSN (Print)1543-7221

Other

Other18th ACM Conference on Computer and Communications Security, CCS'11
CountryUnited States
CityChicago, IL
Period10/17/1110/21/11

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications

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