Detecting Patterns of Appliances from Total Load Data Using a Dynamic Programming Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Remote timing attacks are practical
SSYM'03 Proceedings of the 12th conference on USENIX Security Symposium - Volume 12
Unified Architecture for Large-Scale Attested Metering
HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences
Security Property Violation in CPS through Timing
ICDCSW '08 Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems Workshops
Collaborative Recommender Systems for Building Automation
HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences
Security and Privacy Challenges in the Smart Grid
IEEE Security and Privacy
False data injection attacks against state estimation in electric power grids
Proceedings of the 16th ACM conference on Computer and communications security
Inferring Personal Information from Demand-Response Systems
IEEE Security and Privacy
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
Minimizing private data disclosures in the smart grid
Proceedings of the 2012 ACM conference on Computer and communications security
Neighborhood watch: security and privacy analysis of automatic meter reading systems
Proceedings of the 2012 ACM conference on Computer and communications security
Scaling distributed energy storage for grid peak reduction
Proceedings of the fourth international conference on Future energy systems
Protection of consumer data in the smart grid compliant with the German smart metering guideline
Proceedings of the first ACM workshop on Smart energy grid security
| Hi-index | 0.00 |
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.