TY - CHAP
T1 - Privacy-Preserving Smart Grid Tariff Decisions with Blockchain-Based Smart Contracts
AU - Knirsch, F.
AU - Unterweger, A.
AU - Eibl, G.
AU - Engel, D.
N1 - Cited By :31
Export Date: 14 December 2023
Correspondence Address: Unterweger, A.; Josef Ressel Center for User-Centric Smart Grid Privacy, Urstein Süd 1, Austria; email: [email protected]
References: Karg, L., Kleine-Hegermann, K., Wedler, M., Jahn, C., (2014) E-Energy Abschlussbericht-Ergebnisse und Erkenntnisse aus der Evaluation der sechs Leuchtturmprojekte, Bundesministerium fr Wirtschaft und Technologie, , http://www.digitale-technologien.de/DT/Redaktion/DE/Downloads/ab-gesamt-begleitforschung.pdf?__blob=publicationFile&v=4, (German federal ministry for economy and technology), Tech. Rep., in German [Online]; Lisovich, M., Mulligan, D., Wicker, S., Inferring personal information from demand-response systems (2010) IEEE Secur Priv, 8 (1), pp. 11-20; McKenna, E., Richardson, I., Thomson, M., Smart meter data: Balancing consumer privacy concerns with legitimate applications (2012) Energy Policy, 41, pp. 807-814; Unterweger, A., Knirsch, F., Eibl, G., Engel, D., Privacy-preserving load profile matching for tariff decisions in smart grids (2016) EURASIP J Inf Secur, 2016 (1), p. 21; Nakamoto, S., (2008) Bitcoin: A peer-to-peer electronic cash system, pp. 1-9. , https://bitcoin.org/bitcoin.pdf, Bitcoin.org, [Online]; Wood, G., Ethereum: A secure decentralised generalised transaction ledger (2017) Ethereum, Tech. Rep, , https://ethereum.github.io/yellowpaper/paper.pdf, [Online]; McDaniel, P., McLaughlin, S., Security and privacy challenges in the smart grid (2009) IEEE Secur Priv Mag, 7 (3), pp. 75-77; Eibl, G., Engel, D., Influence of data granularity on smart meter privacy. IEEE Trans (2015) Smart Grid, 6 (2), pp. 930-939; Rane, S.D., Boufounos, P., Privacy-preserving nearest neighbor methods: Comparing signals without revealing them (2013) IEEE Signal Process Mag, 30 (2), pp. 18-28; Kilian, J., Founding cryptography on oblivious transfer (1988) ACM symposium on theory of computing, pp. 20-31. , Chicago: ACM;; Mukherjee, S., Chen, Z., Gangopadhyay, A., A privacy-preserving technique for Euclidean distance-based mining algorithms using Fourier-related transforms (2006) VLDB J, 15 (4), pp. 293-315; Ravikumar, P., Cohen, W.W., Fienberg, S.E., A secure protocol for computing string distance metrics (2004) International conference on data mining (ICDM), pp. 40-46; Wong, W.K., Cheung, D.W.L., Kao, B., Mamoulis, N., Secure kNN computation on encrypted databases categories and subject descriptors (2009) Proceedings of the 35th SIGMOD international conference on management of data, pp. 139-152. , http://doi.acm.org/10.1145/1559845.1559862, [Online]; Boufounos, P.T., Rane, S., (2013) Efficient coding of signal distances using universal quantized embeddings, pp. 251-260. , 2013 Data compression conference (DCC); Cheon, J.H., Kim, M., Lauter, K., (2015) Homomorphic computation of edit distance, 8976, pp. 194-212. , Berlin/Heidelberg: Springer;; Erkin, Z., Veugen, T., Toft, T., Lagendijk, R.L., Generating private recommendations efficiently using homomorphic encryption and data packing (2012) IEEE Trans Inf Forensics Secur, 7 (3), pp. 1053-1066; Rane, S.D., Sun, W., Vetro, A., Secure distortion computation among untrusting parties using homomorphic encryption (2009) 2009 16th IEEE international conference on image processing (ICIP), pp. 1485-1488; Barni, M., Bianchi, T., Catalano, D., Di Raimondo, M., Labati, R.D., Failla, P., (2010) A piivacy- compliant fingerprint recognition system based on homomorphic encryption and fingercode templates, pp. 1-7. , In: IEEE 4th international conference on biometrics: theory, applications and systems (BTAS).2010; Sadeghi, A.R., Schneider, T., Wehrenberg, I., Efficient privacy-preserving face recognition (2010) Information, security and cryptology (ICISC 2009), pp. 229-244. , Lee D, Hong S, editors. Lecture notes in computer science, vol. 5984. Berlin/Heidelberg: Springer;; Kolesnikov, V., Sadeghi, A.R., Schneider, T., Improved garbled circuit building blocks and applications to auctions and computing minima (2009) Cryptology and network security (CANS 2009), pp. 1-20. , Garay JA, Miyaji A, Otsuka A, editors. Lecture notes in computer science, vol. 5888. Berlin/Heidelberg: Springer;; Ben-Sasson, E., Chiesa, A., Garman, C., Green, M., Miers, I., Tromer, E., (2014) Zerocash: Decentralized anonymous payments from bitcoin, pp. 459-474. , In: Proceedings-IEEE Symposium on Security and Privacy. IEEE;; Zyskind, G., Nathan, O., Pentland, A.S., Decentralizing privacy: Using blockchain to protect personal data (2015) Proceedings-2015 IEEE security and privacy workshops (SPW 2015), pp. 180-184; Kosba, A., Miller, A., Shi, E., Wen, Z., Papamanthou, C., Hawk: The blockchain model of cryptography and privacy-preserving smart contracts (2016) 2016 IEEE symposium on security and privacy (SP), pp. 839-858. , IEEE;; Yao, A.C.C., How to generate and exchange secrets (1986) 27th annual symposium on foundations of computer science, pp. 162-167. , Washington: IEEE Computer Society;; Catalano, D., Cramer, R., DiCrescenzo, G., Darmgard, I., Pointcheval, D., Takagi, T., (2005) Provable security for public key schemes, , Basel: Birkhäuser Verlag;; Palensky, P., Dietrich, D., Demand side management: Demand response, intelligent energy systems, and smart loads (2011) IEEE Trans Ind Inf, 7 (3), pp. 381-388; Caron, S., Kesidis, G., Incentive-based energy consumption scheduling algorithms for the smart grid (2010) 2010 First IEEE international conference on smart grid communications (SmartGridComm), pp. 391-396; Shao, S., Zhang, T., Pipattanasomporn, M., Rahman, S., Impact of TOU rates on distribution load shapes in a smart grid with PHEV penetration (2010) 2010 IEEE PES transmission and distribution conference and exposition: Smart solutions for a changing world, pp. 1-6; Ramchurn, S., Vytelingum, P., Rogers, A., Jennings, N., Agent-based control for decentralised demand side management in the smart grid (2011) The 10th international conference on autonomous agents and multiagent systems, AAMAS '11, 1, pp. 5-12. , http://eprints.soton.ac.uk/271985/, Taipei: International Foundation for Autonomous Agents and Multiagent Systems; Mohsenian-Rad, A.H., Wong, V.W.S., Jatskevich, J., Schober, R., Leon-Garcia, A., Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Trans (2010) Smart Grid, 1 (3), pp. 320-331; Knirsch, F., Privacy enhancing technologies in the smart grid user domain. it-Inf Technol (2017) (Thematic Issue: Recent Trends in Energy Informatics Research), 59 (1), pp. 13-22; Gennaro, R., Katz, J., Krawczyk, H., Rabin, T., Secure network coding over the integers (2010) Public key cryptography (PKC 2010), pp. 142-160. , Pointcheval D, Nguyen PQ editors. Lecture notes in computer science, vol. 6056. Berlin/Heidelberg: Springer;; Fiore, D., Gennaro, R., Pastro, V., Efficiently verifiable computation on encrypted data (2014) Proceedings of the 2014 ACM SIGSAC conference on computer and communications security, CCS '14, pp. 844-855. , Scottsdale: ACM;; Parakh, A., (2006) Oblivious transfer using elliptic curves, pp. 323-328. , In: 15th international conference on computing. IEEE; Barker, A., (2016) NIST special publication 800-57: Recommendation for key management-part 1: General, , http://csrc.nist.gov/publications/nistpubs/800-57/sp800-57-Part1-revised2_Mar08-2007.pdf, (revised).[Online]; Peters, G.W., Panayi, E., Understanding modern banking ledgers through blockchain technologies: Future of transaction processing and smart contracts on the internet of money (2016) Banking beyond banks and money: A guide to banking services in the twenty-first century, pp. 239-278. , Paolo T, Aste T, Pelizzon L, Perony N, editors. Cham: Springer International Publishing;; Delmolino, K., Arnett, M., Kosba, A.E., Miller, A., Shi, E., Step by step towards creating a safe smart contract: Lessons and insights from a cryptocurrency lab (2016) Financial cryptography and data security, pp. 79-94. , Barbados: International Financial Cryptography Association;; Pedersen, T.P., Non-interactive and information-theoretic secure verifiable secret sharing (1992) Advances in cryptology (Crypto '91), 91, pp. 129-140; (2012) ITU-T, Recommendation ITU-T X.509-information technology-open systems interconnection-the directory: Public-key and attribute certificate frameworks; (2001) Specification for the advanced encryption standard (AES); Lagendijk, R., Erkin, Z., Barni, M., Encrypted signal processing for privacy protection (2013) IEEE Signal Process Mag, 30, pp. 82-105; Paillier, P., Public-key cryptosystems based on composite degree residuosity classes (1999) Advances in cryptology-EUROCRYPT '99: International conference on the theory and application of cryptographic techniques Prague, Czech Republic, May 2-6, 1999 proceedings, Lecture notes in computer science, vol, pp. 223-238. , Stern J, editor. 1592. Berlin/Heidelberg: Springer;; Barker, E., Barker, W., Burr, W., Polk, W., Smid, M., Division, C.S., (2012) NIST 800-57: Computer security, pp. 1-147; Unterweger, A., Engel, D., Resumable load data compression in smart grids (2015) IEEE Trans Smart Grid, 6 (2), pp. 919-929. , http://dx.doi.org/10.1109/TSG.2014.2364686, [Online]
PY - 2017
Y1 - 2017
N2 - The smart grid changes the way how energy and information are exchanged and offers opportunities for incentive-based load balancing. For instance, customers may shift the time of energy consumption of household appliances in exchange for a cheaper energy tariff. This paves the path towards a full range of modular tariffs and dynamic pricing that incorporate the overall grid capacity as well as individual customer demands. This also allows customers to frequently switch within a variety of tariffs from different utility providers based on individual energy consumption and provision forecasts. For automated tariff decisions it is desirable to have a tool that assists in choosing the optimum tariff based on a prediction of individual energy need and production. However, the revelation of individual load patterns for smart grid applications poses severe privacy threats for customers as analyzed in depth in literature. Similarly, accurate and fine-grained regional load forecasts are sensitive business information of utility providers that are not supposed to be released publicly. This paper extends previous work in the domain of privacy-preserving load profile matching where load profiles from utility providers and load profile forecasts from customers are transformed in a distance-preserving embedding in order to find a matching tariff. The embeddings neither reveal individual contributions of customers nor those of utility providers. Prior work requires a dedicated entity that needs to be trustworthy at least to some extent for determining the matches. In this paper we propose an adaption of this protocol, where we use blockchains and smart contracts for this matching process, instead. Blockchains are gaining widespread adaption in the smart grid domain as a powerful tool for public commitments and accountable calculations. The use of a blockchain for this protocol makes the calculations for tariff matching public, while still maintaining the privacy through embeddings. Further, such decentralized and trust-free blockchains improve the existing solution in terms of verifiability, reliability, and transparency.
AB - The smart grid changes the way how energy and information are exchanged and offers opportunities for incentive-based load balancing. For instance, customers may shift the time of energy consumption of household appliances in exchange for a cheaper energy tariff. This paves the path towards a full range of modular tariffs and dynamic pricing that incorporate the overall grid capacity as well as individual customer demands. This also allows customers to frequently switch within a variety of tariffs from different utility providers based on individual energy consumption and provision forecasts. For automated tariff decisions it is desirable to have a tool that assists in choosing the optimum tariff based on a prediction of individual energy need and production. However, the revelation of individual load patterns for smart grid applications poses severe privacy threats for customers as analyzed in depth in literature. Similarly, accurate and fine-grained regional load forecasts are sensitive business information of utility providers that are not supposed to be released publicly. This paper extends previous work in the domain of privacy-preserving load profile matching where load profiles from utility providers and load profile forecasts from customers are transformed in a distance-preserving embedding in order to find a matching tariff. The embeddings neither reveal individual contributions of customers nor those of utility providers. Prior work requires a dedicated entity that needs to be trustworthy at least to some extent for determining the matches. In this paper we propose an adaption of this protocol, where we use blockchains and smart contracts for this matching process, instead. Blockchains are gaining widespread adaption in the smart grid domain as a powerful tool for public commitments and accountable calculations. The use of a blockchain for this protocol makes the calculations for tariff matching public, while still maintaining the privacy through embeddings. Further, such decentralized and trust-free blockchains improve the existing solution in terms of verifiability, reliability, and transparency.
KW - Blockchain
KW - Electric power transmission networks
KW - Smart power grids
KW - Privacy preserving
KW - Smart contracts
KW - Smart grid
KW - Data privacy
U2 - 10.1007/978-3-319-62238-5_4
DO - 10.1007/978-3-319-62238-5_4
M3 - Chapter
SN - 978-3-319-62237-8
SP - 85
EP - 116
BT - Sustainable Cloud and Energy Services
PB - Springer International Publishing AG
ER -
SDG 7 Affordable and Clean Energy