research-article

Privacy-preserving smart metering

Authors:

George DanezisAuthors Info & Claims

WPES '11: Proceedings of the 10th annual ACM workshop on Privacy in the electronic society

Pages 49 - 60

https://doi.org/10.1145/2046556.2046564

Published: 17 October 2011 Publication History

Abstract

Smart grid proposals threaten user privacy by potentially disclosing fine-grained consumption data to utility providers, primarily for time-of-use billing, but also for profiling, settlement, forecasting, tariff and energy efficiency advice. We propose a privacy-preserving protocol for general calculations on fine-grained meter readings, while keeping the use of tamper evident meters to a strict minimum. We allow users to perform and prove the correctness of computations based on readings on their own devices, without disclosing any fine grained consumption. Applying the protocols to time-of-use billing is particularly simple and efficient, but we also support a wider variety of tariff policies. Cryptographic proofs and multiple implementations are used to show the proposed protocols are secure and efficient.

References

[1]

Mihhail Aizatulin, Andrew D. Gordon, and Jan Jürjens. Extracting and verifying cryptographic models from c protocol code by symbolic execution. 2011.

[2]

Ross Anderson and Shailendra Fuloria. On the security economics of electricity metering. In The Ninth Workshop on the Economics of Information Security, 2010.

[3]

Josep Balasch, Alfredo Rial, Carmela Troncoso, Bart Preneel, Ingrid Verbauwhede, and Christophe Geuens. Pretp: Privacy-preserving electronic toll pricing. In 19th Usenix Security Symposium, August 2010.

Digital Library

[4]

Mihir Bellare and Oded Goldreich. On defining proofs of knowledge. In Ernest F. Brickell, editor, CRYPTO '92, volume 740, pages 390--420. Springer-Verlag, 1992.

Digital Library

[5]

Fabrice Boudot. Efficient proofs that a committed number lies in an interval. In Bart Preneel, editor, EUROCRYPT, volume 1807 of LNCS, pages 431--444. Springer, 2000.

Digital Library

[6]

J. Camenisch and A. Lysyanskaya. A signature scheme with efficient protocols. In SCN 2002, volume 2576 of LNCS, pages 268--289. Springer, 2002.

Digital Library

[7]

J. Camenisch and M. Stadler. Proof systems for general statements about discrete logarithms. Technical Report TR 260, Institute for Theoretical Computer Science, ETH Zürich, March 1997.

[8]

R. Canetti. Universally composable security: A new paradigm for cryptographic protocols. In FOCS, pages 136--145, 2001.

Digital Library

[9]

Ann Cavoukian, Jules Polonetsky, and Christopher Wolf. Smartprivacy for the smart grid: embedding privacy into the design of electricity conservation. In Identity in the Information Society, 2009.

[10]

D. Chaum and T. Pedersen. Wallet databases with observers. In CRYPTO '92, volume 740 of LNCS, pages 89--105, 1993.

Digital Library

[11]

R. Cramer, I. Damgård, and B. Schoenmakers. Proofs of partial knowledge and simplified design of witness hiding protocols. In CRYPTO, pages 174--187, 1994.

Digital Library

[12]

Colette Cuijpers. No to mandatory smart metering does not equal privacy!

[13]

G. Danezis, M. Kohlweiss, and A. Rial. Differentially private billing with rebates. Information Hiding, 2011.

Digital Library

[14]

W. de Jonge and B. Jacobs. Privacy-friendly electronic traffic pricing via commits. In P. Degano, J. Guttman, and F. Martinelli, editors, Formal Aspects in Security and Trust, volume 5491 of LNCS, pages 143--161. Springer, 2008.

Digital Library

[15]

Morris Dwork. Cryptographic protocols of the identity mixer library, v. 2.3.0. IBM research report RZ3730.

[16]

Costas Efthymiou and Georgios Kalogridis. Smart grid privacy via anonymization of smart metering data. In First IEEE International Conference on Smart Grid Communications. IEEE, October, 4-6 2010.

[17]

Omid Fatemieh, Ranveer Chandra, and Carl A. Gunter. Low cost and secure smart meter communications using the tv white spaces. ISRCS '10: IEEE International Symposium on Resilient Control Systems, August. 2010.

[18]

A. Fiat and A. Shamir. How to prove yourself: Practical solutions to identification and signature problems. In CRYPTO, pages 186--194, 1986.

Digital Library

[19]

Flavio D. Garcia and Bart Jacobs. Privacy-friendly energy-metering via homomorphic encryption. Technical report, Radboud Universiteit Nijmegen, February 2010.

[20]

S. Goldwasser, S. Micali, and R. Rivest. A digital signature scheme secure against adaptive chosen-message attacks. SIAM J. Comput., 17(2):281--308, 1988.

Digital Library

[21]

J. Groth. Non-interactive zero-knowledge arguments for voting. In ACNS, pages 467--482, 2005.

Digital Library

[22]

Amir hamed Mohsenian-rad, Vincent W. S. Wong, Juri Jatskevich, and Robert Schober. 1 optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid.

[23]

George W. Hart. Nonintrusive appliance load monitoring. In Proceedings of the IEEE, pages 1870--1891, December 1992.

[24]

Marek Jawurek, Martin Johns, and Florian Kerschbaum. Plug-in privacy for smart metering billing. CoRR, abs/1012.2248, 2010.

[25]

Prachi Kumari, Florian Kelbert, and Alexander Pretschner. Data protection in heterogeneous distributed systems: A smart meter example. In Dependable Software for Critical Infrastructures, October 2011.

[26]

K. Kursawe, M. Kohlweiss, and G. Danezis. Privacy-friendly aggregation for the smart-grid. Privacy Enhancing Technologies, 2011.

Digital Library

[27]

C. Laughman, Kwangduk Lee, R. Cox, S. Shaw, S. Leeb, L. Norford, and P. Armstrong. Power signature analysis. Power and Energy Magazine, IEEE, (2):56--63.

[28]

Michael LeMay, George Gross, Carl A. Gunter, and Sanjam Garg. Unified architecture for large-scale attested metering. In Hawaii International Conference on System Sciences, Big Island, Hawaii, January 2007. ACM.

Digital Library

[29]

Mikhail Lisovich and Stephen Wicker. Privacy concerns in upcoming residential and commercial demand-response systems. In 2008 Clemson University Power Systems Conference. Clemson University, March 2008.

[30]

Patrick McDaniel and Stephen McLaughlin. Security and privacy challenges in the smart grid. IEEE Security and Privacy, 7:75--77, 2009.

Digital Library

[31]

Stephen McLaughlin, Patrick McDaniel, and Dmitry Podkuiko. Energy theft in the advanced metering infrastructure. In 4th International Workshop on Critical Information Infraestructures Security, 2009.

Digital Library

[32]

Andrés Molina-Markham, Prashant Shenoy, Kevin Fu, Emmanuel Cecchet, and David Irwin. Private memoirs of a smart meter. In BuildSys '10. ACM, 2010.

Digital Library

[33]

T. Okamoto. An efficient divisible electronic cash scheme. In CRYPTO, pages 438--451, 1995.

Digital Library

[34]

Elias L. Quinn. Privacy and the new energy infrastructure. SSRN eLibrary, 2009.

[35]

C. Schnorr. Efficient signature generation for smart cards. Journal of Cryptology, 4(3):239--252, 1991.

Digital Library

[36]

N. Swamy, J. Chen, C. Fournet, P.Y. Strub, and K.B.J. Yang. Secure distributed programming with value-dependent types. Technical Report MSR-TR-2010-149, Microsoft Research Cambridge, November 2010.

[37]

The Smart Grid Interoperability Panel. Smart Grid Cyber Security Strategy and Requirements. Technical Report 7628, National Institute of Standards and Technology.

[38]

Carmela Troncoso, George Danezis, Eleni Kosta, and Bart Preneel. Pripayd: privacy friendly pay-as-you-drive insurance. In Peng Ning and Ting Yu, editors, WPES, pages 99--107. ACM, 2007.

Digital Library

[39]

Andreas Wagner, Sebastian Speiser, Oliver Raabe, and Andreas Harth. Linked data for a privacy-aware smart grid. In INFORMATIK 2010 Workshop - Informatik für die Energiesysteme der Zukunft, 2010.

Cited By

Laufer ELevis PRajagopal R(2024)Privacy-Preserving Control of Partitioned Energy ResourcesProceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems10.1145/3632775.3661988(610-624)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3632775.3661988
Kumar JSingh A(2024)A secure paillier cryptosystem based privacy-preserving data aggregation and query processing models for smart gridCluster Computing10.1007/s10586-024-04350-5Online publication date: 23-Mar-2024
https://doi.org/10.1007/s10586-024-04350-5
More KPramod D(2024)Attribute-Based Encryption for the Internet of Things: A ReviewCommunication and Intelligent Systems10.1007/978-981-97-2079-8_26(335-358)Online publication date: 11-May-2024
https://doi.org/10.1007/978-981-97-2079-8_26
Show More Cited By

Index Terms

Privacy-preserving smart metering

Recommendations

A practical smart metering system supporting privacy preserving billing and load monitoring
ACNS'12: Proceedings of the 10th international conference on Applied Cryptography and Network Security

Fine-grained meter readings enable applications in an advanced metering infrastructure. However, those meter readings threaten personal privacy by implying a sketch of daily activities of households. The privacy issue has been addressed in smart ...
An efficient privacy-preserving comparison protocol in smart metering systems

In smart grids, providing power consumption statistics to the customers and generating recommendations for managing electrical devices are considered to be effective methods that can help to reduce energy consumption. Unfortunately, providing power ...
Privacy-preserving smart metering revisited

Privacy-preserving billing protocols are useful in settings where a meter measures user consumption of some service, such as smart metering of utility consumption, pay-as-you-drive insurance and electronic toll collection. In such settings, service ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

WPES '11: Proceedings of the 10th annual ACM workshop on Privacy in the electronic society

October 2011

192 pages

ISBN:9781450310024

DOI:10.1145/2046556

General Chair:
Yan Chen
Northwestern University, USA
,
Program Chair:
Jaideep Vaidya
Rutgers University, 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, 2011

Illinois, Chicago, USA

Acceptance Rates

Overall Acceptance Rate 106 of 355 submissions, 30%

Upcoming Conference

CCS '24

Sponsor:
sigsac

ACM SIGSAC Conference on Computer and Communications Security

October 14 - 18, 2024

Salt Lake City , UT , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

230
Total Citations
View Citations
2,404
Total Downloads

Downloads (Last 12 months)53
Downloads (Last 6 weeks)2

Reflects downloads up to 24 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Laufer ELevis PRajagopal R(2024)Privacy-Preserving Control of Partitioned Energy ResourcesProceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems10.1145/3632775.3661988(610-624)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3632775.3661988
Kumar JSingh A(2024)A secure paillier cryptosystem based privacy-preserving data aggregation and query processing models for smart gridCluster Computing10.1007/s10586-024-04350-5Online publication date: 23-Mar-2024
https://doi.org/10.1007/s10586-024-04350-5
More KPramod D(2024)Attribute-Based Encryption for the Internet of Things: A ReviewCommunication and Intelligent Systems10.1007/978-981-97-2079-8_26(335-358)Online publication date: 11-May-2024
https://doi.org/10.1007/978-981-97-2079-8_26
Sampaio SSousa PMartins CFerreira AAntunes LCruz-Correia R(2023)Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart CitiesApplied Sciences10.3390/app1306383013:6(3830)Online publication date: 16-Mar-2023
https://doi.org/10.3390/app13063830
Wagh GMishra S(2023)A distributed approach to privacy-preservation and integrity assurance of smart metering dataProceedings of the 14th ACM International Conference on Future Energy Systems10.1145/3575813.3576876(60-65)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3575813.3576876
Sang LHu QXu YWu Z(2023)Privacy-Preserving Hybrid Cloud Framework for Real-Time TCL-Based Demand ResponseIEEE Transactions on Cloud Computing10.1109/TCC.2022.314200911:2(1182-1193)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TCC.2022.3142009
Lycklama HBurkhalter LViand AKüchler NHithnawi A(2023)RoFL: Robustness of Secure Federated Learning2023 IEEE Symposium on Security and Privacy (SP)10.1109/SP46215.2023.10179400(453-476)Online publication date: May-2023
https://doi.org/10.1109/SP46215.2023.10179400
Corradini FMarcelletti AMorichetta APolini ARe BTiezzi F(2023)A Flexible Approach to Multi-party Business Process Execution on BlockchainFuture Generation Computer Systems10.1016/j.future.2023.05.006147:C(219-234)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.future.2023.05.006
Gudkov APopov PRomanov S(2023)BalCon — resource balancing algorithm for VM consolidationFuture Generation Computer Systems10.1016/j.future.2023.05.001147:C(265-274)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.future.2023.05.001
Macià SMartínez-Ferrer PAyguadé EBeltran V(2023)Assessing Saiph, a task-based DSL for high-performance computational fluid dynamicsFuture Generation Computer Systems10.1016/j.future.2023.04.035147:C(235-250)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.future.2023.04.035
Show More Cited By

View Options

Get Access

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