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.
- Mihhail Aizatulin, Andrew D. Gordon, and Jan Jürjens. Extracting and verifying cryptographic models from c protocol code by symbolic execution. 2011.Google Scholar
- Ross Anderson and Shailendra Fuloria. On the security economics of electricity metering. In The Ninth Workshop on the Economics of Information Security, 2010.Google Scholar
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- J. Camenisch and A. Lysyanskaya. A signature scheme with efficient protocols. In SCN 2002, volume 2576 of LNCS, pages 268--289. Springer, 2002. Google ScholarDigital Library
- 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.Google Scholar
- R. Canetti. Universally composable security: A new paradigm for cryptographic protocols. In FOCS, pages 136--145, 2001. Google ScholarDigital Library
- 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.Google Scholar
- D. Chaum and T. Pedersen. Wallet databases with observers. In CRYPTO '92, volume 740 of LNCS, pages 89--105, 1993. Google ScholarDigital Library
- R. Cramer, I. Damgård, and B. Schoenmakers. Proofs of partial knowledge and simplified design of witness hiding protocols. In CRYPTO, pages 174--187, 1994. Google ScholarDigital Library
- Colette Cuijpers. No to mandatory smart metering does not equal privacy!Google Scholar
- G. Danezis, M. Kohlweiss, and A. Rial. Differentially private billing with rebates. Information Hiding, 2011. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- Morris Dwork. Cryptographic protocols of the identity mixer library, v. 2.3.0. IBM research report RZ3730.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- A. Fiat and A. Shamir. How to prove yourself: Practical solutions to identification and signature problems. In CRYPTO, pages 186--194, 1986. Google ScholarDigital Library
- Flavio D. Garcia and Bart Jacobs. Privacy-friendly energy-metering via homomorphic encryption. Technical report, Radboud Universiteit Nijmegen, February 2010.Google Scholar
- 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. Google ScholarDigital Library
- J. Groth. Non-interactive zero-knowledge arguments for voting. In ACNS, pages 467--482, 2005. Google ScholarDigital Library
- 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.Google Scholar
- George W. Hart. Nonintrusive appliance load monitoring. In Proceedings of the IEEE, pages 1870--1891, December 1992.Google ScholarCross Ref
- Marek Jawurek, Martin Johns, and Florian Kerschbaum. Plug-in privacy for smart metering billing. CoRR, abs/1012.2248, 2010.Google Scholar
- 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.Google Scholar
- K. Kursawe, M. Kohlweiss, and G. Danezis. Privacy-friendly aggregation for the smart-grid. Privacy Enhancing Technologies, 2011. Google ScholarDigital Library
- 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.Google Scholar
- 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. Google ScholarDigital Library
- 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.Google Scholar
- Patrick McDaniel and Stephen McLaughlin. Security and privacy challenges in the smart grid. IEEE Security and Privacy, 7:75--77, 2009. Google ScholarDigital Library
- Stephen McLaughlin, Patrick McDaniel, and Dmitry Podkuiko. Energy theft in the advanced metering infrastructure. In 4th International Workshop on Critical Information Infraestructures Security, 2009. Google ScholarDigital Library
- Andrés Molina-Markham, Prashant Shenoy, Kevin Fu, Emmanuel Cecchet, and David Irwin. Private memoirs of a smart meter. In BuildSys '10. ACM, 2010. Google ScholarDigital Library
- T. Okamoto. An efficient divisible electronic cash scheme. In CRYPTO, pages 438--451, 1995. Google ScholarDigital Library
- Elias L. Quinn. Privacy and the new energy infrastructure. SSRN eLibrary, 2009.Google Scholar
- C. Schnorr. Efficient signature generation for smart cards. Journal of Cryptology, 4(3):239--252, 1991.Google ScholarDigital Library
- 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.Google Scholar
- The Smart Grid Interoperability Panel. Smart Grid Cyber Security Strategy and Requirements. Technical Report 7628, National Institute of Standards and Technology.Google Scholar
- 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. Google ScholarDigital Library
- 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.Google Scholar
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 SecurityFine-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