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A distributed approach to privacy-preservation and integrity assurance of smart metering data

Authors:

Sumita MishraAuthors Info & Claims

e-Energy '23: Proceedings of the 14th ACM International Conference on Future Energy Systems

Pages 60 - 65

https://doi.org/10.1145/3575813.3576876

Published: 16 June 2023 Publication History

Abstract

Smart grid service providers collect metering data at frequent intervals for providing grid and billing functionalities. Studies have shown that access to the granular metering data can lead to breaches in customers’ privacy. Several aggregation-based privacy-preserving frameworks for smart metering data have been proposed in the literature. However, these frameworks have either a high computational overhead on resource-constrained smart meters and/or are prone to single points of compromise due to centralized designs. Distributed frameworks with outsourced aggregation can provide the desired functionalities while keeping the framework lightweight for the smart meters. However, these distributed frameworks assume an honest-but-curious adversary, which is not a realistic assumption for outsourced aggregation. This work-in-progress paper proposes a distributed aggregation-based privacy-preserving metering data collection framework under a malicious adversarial model (dishonest majority of aggregators). This framework is capable of verifying the integrity of the spatio-temporal metering data while ensuring customers’ privacy. The performance analysis of the proposed framework demonstrates that it outperforms a closely related existing framework with similar customer privacy and integrity verification goals. Our results on the computational overhead on smart meters, end-to-end delay, scalability, and resilience against threats to privacy and integrity are presented in this paper.

References

[1]

Massoud Amin. 2013. Energy: The smart-grid solution. Nature 499, 7457 (2013), 145–147.

[2]

Sainath Bandhakavi, Greg Thompson, and Joseph Beer. 2022. Application integration in utility smart metering using AWS. Retrieved November 20, 2022 from https://aws.amazon.com/blogs/industries/application-integration-in-utility-smart-metering-using-aws/

[3]

Scott Becker. 2018. Estimated vs. Actual Readings – How They Affect Your NYSEG Bill. https://blog.solstice.us/solstice-blog/nyseg-bill-estimated-versus-actual-readings

[4]

Samaresh Bera, Sudip Misra, and Joel JPC Rodrigues. 2014. Cloud computing applications for smart grid: A survey. IEEE Transactions on Parallel and Distributed Systems 26, 5 (2014), 1477–1494.

Digital Library

[5]

Jens-Matthias Bohli, Christoph Sorge, and Osman Ugus. 2010. A privacy model for smart metering. In IEEE International Conference on Communications Workshops. IEEE, Cape Town, South Africa, 1–5.

[6]

Fábio Borges, Denise Demirel, Leon Böck, Johannes Buchmann, and Max Mühlhäuser. 2014. A privacy-enhancing protocol that provides in-network data aggregation and verifiable smart meter billing. In Symposium on Computers and Communications. IEEE, Madeira, Portugal, 1–6.

[7]

Ronald Cramer, Ivan Bjerre Damgård, and Jesper Buus Nielsen. 2015. Secure multiparty computation. Cambridge University Press, Cambridge, United Kingdom.

[8]

Fábio Borges de Oliveira. 2017. On privacy-preserving protocols for smart metering systems. Springer, Switzerland.

[9]

Susen Döbelt, Markus Jung, Marc Busch, and Manfred Tscheligi. 2015. Consumers’ privacy concerns and implications for a privacy preserving Smart Grid architecture—Results of an Austrian study. Energy Research & Social Science 9 (2015), 137–145.

[10]

Yihui Dong, Jian Shen, Sai Ji, Rongxin Qi, and Shuai Liu. 2021. A novel appliance-based secure data aggregation scheme for bill generation and demand management in smart grids. Connection Science 33, 4 (2021), 1116–1137.

[11]

Costas Efthymiou and Georgios Kalogridis. 2010. Smart grid privacy via anonymization of smart metering data. In International Conference on Smart Grid Communications. IEEE, Maryland, United States, 238–243.

[12]

Zekeriya Erkin and Gene Tsudik. 2012. Private computation of spatial and temporal power consumption with smart meters. In International Conference on Applied Cryptography and Network Security. Springer, Singapore, 561–577.

Digital Library

[13]

Chun-I Fan, Shi-Yuan Huang, and Yih-Loong Lai. 2013. Privacy-enhanced data aggregation scheme against internal attackers in smart grid. IEEE Transactions on Industrial informatics 10, 1 (2013), 666–675.

[14]

A. Faruqui and C. Bourbonnais. 2020. The Tariffs of Tomorrow: Innovations in Rate Designs. IEEE Power and Energy Magazine 18, 3 (2020), 18–25.

[15]

Prosanta Gope and Biplab Sikdar. 2018. Lightweight and privacy-friendly spatial data aggregation for secure power supply and demand management in smart grids. IEEE Transactions on Information Forensics and Security 14, 6 (2018), 1554–1566.

[16]

Ulrich Greveler, Peter Glösekötterz, Benjamin Justusy, and Dennis Loehr. 2012. Multimedia content identification through smart meter power usage profiles. In Proceedings of the International Conference on Information and Knowledge Engineering. The Steering Committee of The World Congress in Computer Science, Wuyishan,China, 1–8.

[17]

Zhitao Guan, Guanlin Si, Xiaosong Zhang, Longfei Wu, Nadra Guizani, Xiaojiang Du, and Yinglong Ma. 2018. Privacy-preserving and efficient aggregation based on blockchain for power grid communications in smart communities. IEEE Communications Magazine 56, 7 (2018), 82–88.

[18]

Jaap-Henk Hoepman. 2017. Privacy friendly aggregation of smart meter readings, even when meters crash. In Workshop on Cyber-Physical Security and Resilience in Smart Grids. ACM, Pennsylvania, United States, 3–7.

Digital Library

[19]

Malik Ali Judge, Asif Khan, Awais Manzoor, and Hasan Ali Khattak. 2022. Overview of smart grid implementation: Frameworks, impact, performance and challenges. Journal of Energy Storage 49 (2022), 104056.

[20]

Hayat Mohammad Khan, Abid Khan, Farhana Jabeen, and Arif Ur Rahman. 2021. Privacy preserving data aggregation with fault tolerance in fog-enabled smart grids. Sustainable Cities and Society 64 (2021), 102522.

[21]

Fengjun Li and Bo Luo. 2012. Preserving data integrity for smart grid data aggregation. In International Conference on Smart Grid Communications. IEEE, Tainan City, Taiwan, 366–371.

[22]

Hongwei Li, Xiaodong Lin, Haomiao Yang, Xiaohui Liang, Rongxing Lu, and Xuemin Shen. 2014. EPPDR: An efficient privacy-preserving demand response scheme with adaptive key evolution in smart grid. IEEE Transactions on Parallel and Distributed Systems 25, 8 (2014), 2053–2064.

[23]

Kunchang Li, Yifan Yang, Shuhao Wang, Runhua Shi, and Jianbin Li. 2021. A lightweight privacy-preserving and sharing scheme with dual-blockchain for intelligent pricing system of smart grid. Computers & Security 103 (2021), 102189.

[24]

Yining Liu, Wei Guo, Chun-I Fan, Liang Chang, and Chi Cheng. 2018. A practical privacy-preserving data aggregation (3PDA) scheme for smart grid. IEEE Transactions on Industrial Informatics 15, 3 (2018), 1767–1774.

[25]

Dylan Locsin and John Pressley. 2022. Duke Energy and AWS are innovating for a smarter, cleaner energy future. Retrieved November 20, 2022 from https://aws.amazon.com/blogs/industries/duke-energy-and-aws-are-innovating-for-a-smarter-cleaner-energy-future/

[26]

Rongxing Lu. 2016. Privacy-Preserving Multifunctional Data Aggregation. In Privacy-Enhancing Aggregation Techniques for Smart Grid Communications. Springer, Switzerland, 85–110.

[27]

Rongxing Lu, Kevin Heung, Arash Habibi Lashkari, and Ali A Ghorbani. 2017. A lightweight privacy-preserving data aggregation scheme for fog computing-enhanced IoT. IEEE access 5 (2017), 3302–3312.

[28]

Rongxing Lu, Xiaohui Liang, Xu Li, Xiaodong Lin, and Xuemin Shen. 2012. EPPA: An efficient and privacy-preserving aggregation scheme for secure smart grid communications. IEEE Transactions on Parallel and Distributed Systems 23, 9 (2012), 1621–1631.

Digital Library

[29]

Daisuke Mashima, Aidana Serikova, Yao Cheng, and Binbin Chen. 2018. Towards quantitative evaluation of privacy protection schemes for electricity usage data sharing. ICT Express 4, 1 (2018), 35–41.

[30]

Michael Matz. 2021. The Grid is moving to the cloud. Retrieved November 20, 2022 from https://eprijournal.com/the-grid-is-moving-to-the-cloud/

[31]

Akanksha Maurya, Alper Sinan Akyurek, Baris Aksanli, and Tajana Simunic Rosing. 2016. Time-series clustering for data analysis in smart grid. In International Conference on Smart Grid Communications. IEEE, Sydney, Australia, 606–611.

[32]

Andres Molina-Markham, George Danezis, Kevin Fu, Prashant Shenoy, and David Irwin. 2012. Designing privacy-preserving smart meters with low-cost microcontrollers. In International Conference on Financial Cryptography and Data Security. Springer, Heidelberg, Germany, 239–253.

[33]

Andrés Molina-Markham, Prashant Shenoy, Kevin Fu, Emmanuel Cecchet, and David Irwin. 2010. Private memoirs of a smart meter. In ACM Workshop on Embedded Sensing Systems for Energy Efficiency in Building. ACM, Zurich, Switzerland, 61–66.

Digital Library

[34]

Jianbing Ni, Khalid Alharbi, Xiaodong Lin, and Xuemin Shen. 2015. Security-enhanced data aggregation against malicious gateways in smart grid. In Global Communications Conference. IEEE, California, USA, 1–6.

[35]

Kazuma Ohara, Yusuke Sakai, Fumiaki Yoshida, Mitsugu Iwamoto, and Kazuo Ohta. 2014. Privacy-preserving smart metering with verifiability for both billing and energy management. In Workshop on ASIA Public-Key Cryptography. ACM, New York, United States, 23–32.

Digital Library

[36]

Marco Pau, Edoardo Patti, Luca Barbierato, Abouzar Estebsari, Enrico Pons, Ferdinanda Ponci, and Antonello Monti. 2018. A cloud-based smart metering infrastructure for distribution grid services and automation. Sustainable Energy, Grids and Networks 15 (2018), 14–25.

[37]

Torben Pryds Pedersen. 1991. Non-Interactive and Information-Theoretic Secure Verifiable Secret Sharing. In International Cryptology Conference. Springer, California, United States, 129–140.

[38]

Victoria Y Pillitteri and Tanya L Brewer. 2014. NISTIR 7628 Revision 1, Guidelines for Smart Grid Cybersecurity. Smart Grid Interoperability Panel, Smart Grid Cybersecurity Committee (2014), 668.

[39]

Alfredo Rial and George Danezis. 2011. Privacy-preserving smart metering. In ACM workshop on Privacy in the electronic society. ACM, Illinois, United States, 49–60.

Digital Library

[40]

Cristina Rottondi, Giacomo Verticale, and Antonio Capone. 2013. Privacy-preserving smart metering with multiple data consumers. Computer Networks 57, 7 (2013), 1699–1713.

Digital Library

[41]

Cristina Rottondi, Giacomo Verticale, and Christoph Krauss. 2013. Distributed privacy-preserving aggregation of metering data in smart grids. IEEE Journal on Selected Areas in Communications 31, 7 (2013), 1342–1354.

[42]

Adi Shamir. 1979. How to share a secret. Commun. ACM 22, 11 (1979), 612–613.

Digital Library

[43]

Gang Shen, Yixin Su, Danhong Zhang, Cheng Zhang, and Mingwu Zhang. 2019. A robust, distributed, and privacy-preserving aggregation scheme for smart grid communications. Journal of the Chinese Institute of Engineers 42, 1 (2019), 54–65.

[44]

Jingcheng Song, Yining Liu, Jun Shao, and Chunming Tang. 2019. A dynamic membership data aggregation (DMDA) protocol for smart grid. IEEE Systems Journal 14, 1 (2019), 900–908.

[45]

Song Tan, Debraj De, Wen-Zhan Song, Junjie Yang, and Sajal K Das. 2017. Survey of security advances in smart grid: A data driven approach. IEEE Communications Surveys & Tutorials 19, 1 (2017), 397–422.

Digital Library

[46]

Samet Tonyali, Kemal Akkaya, Nico Saputro, A Selcuk Uluagac, and Mehrdad Nojoumian. 2018. Privacy-preserving protocols for secure and reliable data aggregation in IoT-enabled Smart Metering systems. Future Generation Computer Systems 78 (2018), 547–557.

[47]

Gaurav S Wagh, Sahil Gupta, and Sumita Mishra. 2020. A distributed privacy preserving framework for the smart grid. In Innovative Smart Grid Technologies Conference. IEEE, Washington DC, United States, 1–5.

[48]

Gaurav S Wagh and Sumita Mishra. 2020. A cyber-resilient privacy framework for the smart grid with dynamic billing capabilities. In Conference on Communications, Control, and Computing Technologies for Smart Grids. IEEE, Virtual Conference, 1–6.

[49]

Gaurav S Wagh and Sumita Mishra. 2021. Divide & Conquer: A Privacy Safeguarding Framework for the Smart Grid. In Proceedings of the International Conference on Communications. IEEE, Montreal, Canada, 1–6.

[50]

Xiaodi Wang, Yining Liu, and Kim-Kwang Raymond Choo. 2020. Fault-tolerant multisubset aggregation scheme for smart grid. IEEE Transactions on Industrial Informatics 17, 6 (2020), 4065–4072.

[51]

Zhiwei Wang. 2019. Identity-based verifiable aggregator oblivious encryption and its applications in smart grids. IEEE Transactions on Sustainable Computing 6, 1 (2019), 80–89.

[52]

Wenzheng Zhang, Shiyun Liu, and Zhe Xia. 2022. A distributed privacy-preserving data aggregation scheme for smart grid with fine-grained access control. Journal of Information Security and Applications 66 (2022), 103118.

Digital Library

[53]

Xiangjian Zuo, Lixiang Li, Haipeng Peng, Shoushan Luo, and Yixian Yang. 2020. Privacy-preserving multidimensional data aggregation scheme without trusted authority in smart grid. IEEE Systems Journal 15, 1 (2020), 395–406.

Cited By

Wagh GMishra SAgarwal A(2024)A Distributed Privacy-Preserving Framework With Integrity Verification Capabilities for Metering Data and Dynamic Billing in a Malicious SettingIEEE Transactions on Smart Grid10.1109/TSG.2024.343338515:6(5813-5825)Online publication date: Nov-2024
https://doi.org/10.1109/TSG.2024.3433385
Wu JLu CWu CShi JGonzalez MWang DHan Z(2024)A cluster-based appliance-level-of-use demand response program designApplied Energy10.1016/j.apenergy.2024.123003362(123003)Online publication date: May-2024
https://doi.org/10.1016/j.apenergy.2024.123003

Index Terms

A distributed approach to privacy-preservation and integrity assurance of smart metering data
1. Security and privacy
  1. Security services
    1. Privacy-preserving protocols

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e-Energy '23: Proceedings of the 14th ACM International Conference on Future Energy Systems

June 2023

545 pages

ISBN:9798400700323

DOI:10.1145/3575813

Copyright © 2023 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]

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Published: 16 June 2023

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e-Energy '23: The 14th ACM International Conference on Future Energy Systems

June 20 - 23, 2023

FL, Orlando, USA

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Cited By

Wagh GMishra SAgarwal A(2024)A Distributed Privacy-Preserving Framework With Integrity Verification Capabilities for Metering Data and Dynamic Billing in a Malicious SettingIEEE Transactions on Smart Grid10.1109/TSG.2024.343338515:6(5813-5825)Online publication date: Nov-2024
https://doi.org/10.1109/TSG.2024.3433385
Wu JLu CWu CShi JGonzalez MWang DHan Z(2024)A cluster-based appliance-level-of-use demand response program designApplied Energy10.1016/j.apenergy.2024.123003362(123003)Online publication date: May-2024
https://doi.org/10.1016/j.apenergy.2024.123003

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