More Web Proxy on the site http://driver.im/

research-article

Protecting consumer privacy from electric load monitoring

Authors:

Stephen McLaughlin,

Patrick McDaniel,

William AielloAuthors Info & Claims

CCS '11: Proceedings of the 18th ACM conference on Computer and communications security

Pages 87 - 98

https://doi.org/10.1145/2046707.2046720

Published: 17 October 2011 Publication History

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.

References

[1]

Blue Sky Solar Boost 6024HDL. http://www.ecodirect.com/Blue-Sky-Solar-Boost-6024HL-60Amp-12 -24-Volt-p/blue-sky-solar-boost-6024hdl.htm.

[2]

Rechargeable Sealed Lead Acid Battery. http://www.lowcostbatteries.com/product_p/12v-50ah.htm.

[3]

Baranski, M., and Voss, J. Detecting Patterns of Appliances from Total Load Data Using a Dynamic Programming Approach. IEEE International Conference on Data Mining (2004).

Digital Library

[4]

Baranski, M., and Voss, J. Genetic Algorithm for Pattern Detection in NIALM Systems. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (2004).

[5]

Brumley, D., and Boneh, D. Remote Timing Attacks are Practical. In Proceedings of the 12th USENIX Security Symposium (2003).

Digital Library

[6]

Carson, A. Smart Grids and the Future of Privacy. http://www.greentechmedia.com/articles/read/smart-grids-and-the-future-of-privacy/, 2010.

[7]

Electropedia. Battery Life (and Death). http://www.mpoweruk.com/life.htm.

[8]

Elster. REX2-EA meter. http://www.elstermetering.com, 2010.

[9]

GreenTerraFirma.com. Time Of Use Electrical Pricing. http://greenterrafirma.com/time-of-use-electrical-pricing.html, 2010.

[10]

Guedri, M. E., D'Urso, G., Lajaunie, C., and Fleury, G. Time-Frequency Characterisation for Electric Load Monitoring. In Proceedings of the 17th European Signal Processing Conference (EUSIPCO) (2009).

[11]

Hart, G. W. Residential Energy Monitoring and Computerized Surveillance via Utility Power Flows. IEEE Technology and Society Magazine (1989).

[12]

Hart, G. W. Nonintrusive Appliance Load Monitoring. Proceedings of the IEEE (2004).

[13]

Huang, B., Hsu, P., Wu, M., and Ho, P. System Dynamic Model and Charging Control of Lead-Acid Battery for Stand-Alone Solar PV System. Solar Energy 84 (2010).

[14]

iTron. CENTRON Meter. http://www.itron.com, 2010.

[15]

John, J. S. Smart Grid Data: Too Much For Privacy, Not Enough For Innovation? http://gigaom.com, 2010.

[16]

Kalogridis, G., Efthymiou, C., Denic, S., Lewis, T., and Cepeda, R. Privacy for Smart Meters: Towards Undetectable Appliance Load Signatures. In First IEEE International Conference on Smart Grid Communications (SmartGridComm) (2010).

[17]

King, C. S. The Economics of Real-Time and Time-of-Use Pricing For Residential Consumers. Tech. rep., American Energy Institute, 2001.

[18]

Laughman, C., Lee, K., Cox, R., Shaw, S., Leeb, S., Norford, L., and Armstrong, P. Power signature analysis. IEEE Power and Energy Magazine 1 (2003).

[19]

LeMay, M., Gross, G., Gunter, C. A., and Garg, S. Unified Architecture for Large-Scale Attested Metering. In Hawaii International Conference on System Sciences (2007).

Digital Library

[20]

LeMay, M., Haas, J. J., and Gunter, C. A. Collaborative Recommender Systems for Building Automation. In Hawaii International Conference on System Sciences (2009).

Digital Library

[21]

Leo, A. The measure of power. MIT Technology Review (2001).

[22]

Lisovich, M. A., Mulligan, D. K., and Wicker, S. B. Inferring Personal Information from Demand-Response Systems. IEEE Security and Privacy 8 (2010).

Digital Library

[23]

Litos Strategic Communication. The Smart Grid: An Introduction. http://www.oe.energy.gov/SmartGridIntroduction.htm, 2008.

[24]

Liu, Y., Ning, P., and Reiter, M. K. False Data Injection Attacks against State Estimation in Electric Power Grids. In Proceedings of the 16th ACM Conference on Computer and Communications Security (2009).

Digital Library

[25]

Marceau, M. L., and Zmeureanu, R. Nonintrusive Load Disaggregation Computer Program to Estimate the Energy Consumption of Major End Uses in Residential Buildings. Energy Conversion and Management 41, 13 (2000).

[26]

Mathworks. Simpowersystems 5.3. http://www.mathworks.com/products/simpower/, 2010.

[27]

McDaniel, P., and McLaughlin, S. Security and Privacy Challenges in the Smart Grid. IEEE Security & Privacy Magazine (2009).

Digital Library

[28]

Meritt, R. Stimulus: DoE Readies \$4.3 Billion for Smart Grid. EE Times (2009).

[29]

MeterPedia.com. Meter Manufactuer Data, 2010.

[30]

Orange, and Rockland. Time of Use Rate. http://www.oru.com/programsandservices/incentivesandrebates/timeofuse.h%tml, 2010.

[31]

Pacfic Gas and Electric Company. Electric Schedule E-6, Residential Time-Of-Use Service. http://greenterrafirma.com/time-of-use-electrical-pricing.html, 2010.

[32]

Patel, S. N., Robertson, T., Kientz, J. A., Reynolds, M. S., and Abowd, G. D. At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on the Residential Power Line. In Proceedings of Ubicomp (2007).

Digital Library

[33]

Prudenzi, A. A Neuron Nets Based Procedure for Identifying Domestic Appliances Pattern-of-Use From Energy Recordings at Meter Panel. In Power Engineering Society Winter Meeting (2002).

[34]

Sensus. iCon APX C&I Meter. http://na.sensus.com/, 2010.

[35]

Sultanem, F. Using Appliance Signatures for Monitoring Residential Loads at Meter Panel Level. IEEE Transactions on Power Delivery 6, 4 (1991).

[36]

Tang, H., and McMillin, B. M. Security Property Violation in CPS through Timing. International Conference on Distributed Computing Systems Workshops 0 (2008).

Digital Library

[37]

The Energy Detective. The TED 5000 Overview. http://www.theenergydetective.com/, 2010.

[38]

Tremblay, O., Dessaint, L. A., and Dekkiche, A. A Generic Battery Model for the Dynamic Simulation of Hybrid Electric Vehicles. In 2007 IEEE Vehicle Power and Propulsion Conference (September 2007), IEEE, pp. 284--289.

[39]

US Energy Information Administration. Frequently Asked Questions -- Electricity. http://tonto.eia.doe.gov/ask/electricity_faqs.asp#electricity_use_home, 2010.

[40]

W. K. Lee and G. S. K Fung and H. Y. Lam and F. H. Y. Chan and Mark Lucente. Exploration on Load Signatures. In International Conference on Electrical Engineering (2004).

[41]

Yang, H.-T., Chang, H.-H., and Lin, C.-L. Design a Neural Network for Features Selection in Non-intrusive Monitoring of Industrial Electrical Loads. In 11th International Conference on Computer Supported Cooperative Work in Design (2007).

Cited By

Yao XWu YSu JHuang RTian Y(2024)A Φ-Differential Privacy Scheme for Incentive-Based Demand Response in Smart GridComputational and Experimental Simulations in Engineering10.1007/978-3-031-44947-5_43(537-549)Online publication date: 25-Jan-2024
https://doi.org/10.1007/978-3-031-44947-5_43
Polčák L(2023)Responsible and Safe Home MeteringInformation Security and Privacy in Smart Devices10.4018/978-1-6684-5991-1.ch001(1-40)Online publication date: 31-Mar-2023
https://doi.org/10.4018/978-1-6684-5991-1.ch001
Kua JHossain MNatgunanathan IXiang Y(2023)Privacy Preservation in Smart Meters: Current Status, Challenges and Future DirectionsSensors10.3390/s2307369723:7(3697)Online publication date: 3-Apr-2023
https://doi.org/10.3390/s23073697
Show More Cited By

Index Terms

Protecting consumer privacy from electric load monitoring
1. Applied computing

Recommendations

Minimizing private data disclosures in the smart grid
CCS '12: Proceedings of the 2012 ACM conference on Computer and communications security

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 ...
Protecting privacy in data release
Foundations of security analysis and design VI

The evolution of the Information and Communication Technology has radically changed our electronic lives, making information the key driver for today's society. Every action we perform requires the collection, elaboration, and dissemination of personal ...
Privacy-preserving load profile matching for tariff decisions in smart grids

In liberalized energy markets, matching consumption patterns to energy tariffs is desirable, but practically limited due to privacy concerns, both on the side of the consumer and on the side of the utilities. We propose a protocol through which a ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

CCS '11: Proceedings of the 18th ACM conference on Computer and communications security

October 2011

742 pages

ISBN:9781450309486

DOI:10.1145/2046707

General Chair:
Yan Chen
Northwestern University, USA
,
Program Chairs:
George Danezis
Microsoft Research Cambridge, UK
,
Vitaly Shmatikov
University of Texas at Austin, USA

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGSAC: ACM Special Interest Group on Security, Audit, and Control

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CCS'11

Sponsor:

SIGSAC

CCS'11: the ACM Conference on Computer and Communications Security

October 17 - 21, 2011

Illinois, Chicago, USA

Acceptance Rates

CCS '11 Paper Acceptance Rate 60 of 429 submissions, 14%;

Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

Upcoming Conference

CCS '25

Sponsor:
sigsac

ACM SIGSAC Conference on Computer and Communications Security

October 13 - 17, 2025

Taipei , Taiwan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

190
Total Citations
View Citations
1,606
Total Downloads

Downloads (Last 12 months)48
Downloads (Last 6 weeks)7

Reflects downloads up to 12 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Yao XWu YSu JHuang RTian Y(2024)A Φ-Differential Privacy Scheme for Incentive-Based Demand Response in Smart GridComputational and Experimental Simulations in Engineering10.1007/978-3-031-44947-5_43(537-549)Online publication date: 25-Jan-2024
https://doi.org/10.1007/978-3-031-44947-5_43
Polčák L(2023)Responsible and Safe Home MeteringInformation Security and Privacy in Smart Devices10.4018/978-1-6684-5991-1.ch001(1-40)Online publication date: 31-Mar-2023
https://doi.org/10.4018/978-1-6684-5991-1.ch001
Kua JHossain MNatgunanathan IXiang Y(2023)Privacy Preservation in Smart Meters: Current Status, Challenges and Future DirectionsSensors10.3390/s2307369723:7(3697)Online publication date: 3-Apr-2023
https://doi.org/10.3390/s23073697
Xu WZhang YCardell-Oliver RMian AHong J(2023)Entropy-based Selective Homomorphic Encryption for Smart Metering Systems2023 IEEE 28th Pacific Rim International Symposium on Dependable Computing (PRDC)10.1109/PRDC59308.2023.00035(228-235)Online publication date: 24-Oct-2023
https://doi.org/10.1109/PRDC59308.2023.00035
Gangopadhyay SDas S(2023)Modeling Appliance Usage Privacy of a Group of Consumers using Smart Meter Data2023 IEEE Energy Conversion Congress and Exposition (ECCE)10.1109/ECCE53617.2023.10362754(1522-1529)Online publication date: 29-Oct-2023
https://doi.org/10.1109/ECCE53617.2023.10362754
Zhou YPan TWang LLi R(2023)The Impact of Heterogeneity in Consumers’ Socio-Demographic Characteristics on the Acceptance of Ambient Assisted Living Technology for Older Adults Monitoring in the Home ContextInternational Journal of Human–Computer Interaction10.1080/10447318.2023.219753040:14(3699-3716)Online publication date: 10-Apr-2023
https://doi.org/10.1080/10447318.2023.2197530
Valadares DSobrinho ÁWill NGorgônio KPerkusich A(2023)Trusted and only Trusted. That is the Access!Advanced Information Networking and Applications10.1007/978-3-031-28694-0_47(490-503)Online publication date: 15-Mar-2023
https://doi.org/10.1007/978-3-031-28694-0_47
Chang XLi WXia CYang QMa JYang TZomaya A(2022)Transferable Tree-Based Ensemble Model for Non-Intrusive Load MonitoringIEEE Transactions on Sustainable Computing10.1109/TSUSC.2022.31759417:4(970-981)Online publication date: 1-Oct-2022
https://doi.org/10.1109/TSUSC.2022.3175941
Sheikh NAsghar HFarokhi FKaafar M(2022)Do Auto-Regressive Models Protect Privacy? Inferring Fine-Grained Energy Consumption From Aggregated Model ParametersIEEE Transactions on Services Computing10.1109/TSC.2021.310049815:6(3198-3209)Online publication date: 1-Nov-2022
https://doi.org/10.1109/TSC.2021.3100498
Wang HSi CLiu GZhao JWen FXue Y(2022)Fed‐NILM: A federated learning‐based non‐intrusive load monitoring method for privacy‐protectionEnergy Conversion and Economics10.1049/enc2.120553:2(51-60)Online publication date: 20-Apr-2022
https://doi.org/10.1049/enc2.12055
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents