Privacy-Aware Smart Metering: A Survey

The increasing share of renewables creates new challenges for the existing electrical grid. To deal with these challenges, various efforts are being made to transform the existing power grid into a so-called smart grid. Part of this process is the deployment of an advanced metering infrastructure, which provides novel high-frequency two-way communication between consumers and producers. But as useful as the access to high-frequency measurements may be for energy suppliers, this also poses a major threat to the privacy of the customers. In this survey we present approaches to the problem of customer privacy-protection in the smart grid. We show that the privacy problem in smart grids can be further divided into the problems metering for billing and metering for operations. For each of these problems we identify generic approaches to solve them.

[1]  Wenyuan Xu,et al.  Neighborhood watch: security and privacy analysis of automatic meter reading systems , 2012, CCS.

[2]  Geert Deconinck,et al.  Analysis of State-of-the-art Smart Metering Communication Standards , 2010 .

[3]  G.W. Hart,et al.  Residential energy monitoring and computerized surveillance via utility power flows , 1989, IEEE Technology and Society Magazine.

[4]  Bart Jacobs,et al.  Privacy-Friendly Energy-Metering via Homomorphic Encryption , 2010, STM.

[5]  Ingmar Baumgart,et al.  Elderberry: A peer-to-peer, privacy-aware smart metering protocol , 2013, 2013 Proceedings IEEE INFOCOM.

[6]  M. Lisovich,et al.  Privacy Concerns in Upcoming Residential and Commercial Demand-Response Systems , 2008 .

[7]  Taskin Koçak,et al.  Smart Grid Technologies: Communication Technologies and Standards , 2011, IEEE Transactions on Industrial Informatics.

[8]  Yi Xu,et al.  A survey on the communication architectures in smart grid , 2011, Comput. Networks.

[9]  Prashant J. Shenoy,et al.  Private memoirs of a smart meter , 2010, BuildSys '10.

[10]  Peng Liu,et al.  Secure Information Aggregation for Smart Grids Using Homomorphic Encryption , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[11]  Stephen B. Wicker,et al.  Inferring Personal Information from Demand-Response Systems , 2010, IEEE Security & Privacy.

[12]  George Danezis,et al.  Privacy-preserving smart metering , 2011, WPES '11.

[13]  G. Schwartz,et al.  Privacy-Aware Sampling for Residential Demand Response Programs , 2012 .

[14]  Dirk Westhoff,et al.  Privacy-enhanced architecture for smart metering , 2012, International Journal of Information Security.

[15]  Friedemann Mattern,et al.  Leveraging smart meter data to recognize home appliances , 2012, 2012 IEEE International Conference on Pervasive Computing and Communications.

[16]  Georgios Kalogridis,et al.  Affordable Privacy for Home Smart Meters , 2011, 2011 IEEE Ninth International Symposium on Parallel and Distributed Processing with Applications Workshops.

[17]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.

[18]  Dirk Westhoff,et al.  Homomorphic Primitives for a Privacy-friendly Smart Metering Architecture , 2012, SECRYPT.

[19]  David Chaum,et al.  Untraceable electronic mail, return addresses, and digital pseudonyms , 1981, CACM.

[20]  A. Prudenzi,et al.  A neuron nets based procedure for identifying domestic appliances pattern-of-use from energy recordings at meter panel , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[21]  Andrew Y. Ng,et al.  Energy Disaggregation via Discriminative Sparse Coding , 2010, NIPS.

[22]  Anno Accademico,et al.  Smart Grid Communications: Overview of research challenges, solutions and standardization activities , 2013 .

[23]  Craig Gentry,et al.  Computing arbitrary functions of encrypted data , 2010, CACM.

[24]  F. Sultanem,et al.  Using appliance signatures for monitoring residential loads at meter panel level , 1991 .

[25]  Torben P. Pedersen Non-Interactive and Information-Theoretic Secure Verifiable Secret Sharing , 1991, CRYPTO.

[26]  Xue Liu,et al.  PDA: Privacy-Preserving Data Aggregation in Wireless Sensor Networks , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[27]  David Chaum,et al.  Minimum Disclosure Proofs of Knowledge , 1988, J. Comput. Syst. Sci..

[28]  Georgios Kalogridis,et al.  Privacy for Smart Meters: Towards Undetectable Appliance Load Signatures , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[29]  Christian Wietfeld,et al.  Comparison of the communication protocols DLMS/COSEM, SML and IEC 61850 for smart metering applications , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[30]  H. Y. Lam,et al.  A Novel Method to Construct Taxonomy Electrical Appliances Based on Load Signaturesof , 2007, IEEE Transactions on Consumer Electronics.

[31]  Christoph Sorge,et al.  A Privacy Model for Smart Metering , 2010, 2010 IEEE International Conference on Communications Workshops.

[32]  Peng Liu,et al.  Secure and privacy-preserving information aggregation for smart grids , 2011, Int. J. Secur. Networks.

[33]  Florian Kerschbaum,et al.  Plug-In Privacy for Smart Metering Billing , 2010, PETS.

[34]  Mani B. Srivastava,et al.  Cooperative state estimation for preserving privacy of user behaviors in smart grid , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[35]  Ingmar Baumgart,et al.  Pseudonymous Smart Metering without a Trusted Third Party , 2013, 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications.

[36]  D.G. Hart,et al.  Using AMI to realize the Smart Grid , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[37]  Marek Jawurek,et al.  Smart metering de-pseudonymization , 2011, ACSAC '11.

[38]  Christoph Sorge,et al.  Do not snoop my habits: preserving privacy in the smart grid , 2012, IEEE Communications Magazine.

[39]  Patrick D. McDaniel,et al.  Protecting consumer privacy from electric load monitoring , 2011, CCS '11.

[40]  H. Vincent Poor,et al.  Smart meter privacy: A utility-privacy framework , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[41]  Steven J. Vaughan-Nichols How trustworthy is trusted computing? , 2003, Computer.

[42]  A. Cavoukian,et al.  SmartPrivacy for the Smart Grid: embedding privacy into the design of electricity conservation , 2010 .

[43]  Steven B. Leeb,et al.  Power signature analysis , 2003 .

[44]  Frederik Armknecht,et al.  A lifetime-optimized end-to-end encryption scheme for sensor networks allowing in-network processing , 2008, Comput. Commun..

[45]  Sanjam Garg,et al.  Unified Architecture for Large-Scale Attested Metering , 2007, 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07).

[46]  Mathias Uslar,et al.  Survey of Smart Grid Standardization Studies and Recommendations , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[47]  Georgios Kalogridis,et al.  Smart Grid Privacy via Anonymization of Smart Metering Data , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[48]  A. Pfitzmann,et al.  A terminology for talking about privacy by data minimization: Anonymity, Unlinkability, Undetectability, Unobservability, Pseudonymity, and Identity Management , 2010 .

[49]  Leendert van Doorn,et al.  A Practical Guide to Trusted Computing , 2007 .

[50]  David Pointcheval,et al.  Efficient Public-Key Cryptosystems Provably Secure Against Active Adversaries , 1999, ASIACRYPT.

[51]  Ronald Petrlic,et al.  A privacy-preserving Concept for Smart Grids , 2010 .

[52]  M. Baranski,et al.  Genetic algorithm for pattern detection in NIALM systems , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).