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A verifiable privacy-preserving data collection scheme supporting multi-party computation in fog-based smart grid

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Abstract

Incorporation of fog computing with low latency, preprocession (e.g., data aggregation) and location awareness, can facilitate fine-grained collection of smart metering data in smart grid and promotes the sustainability and efficiency of the grid. Recently, much attention has been paid to the research on smart grid, especially in protecting privacy and data aggregation. However, most previous works do not focus on privacy-preserving data aggregation and function computation query on enormous data simultaneously in smart grid based on fog computation. In this paper, we construct a novel verifiable privacy-preserving data collection scheme supporting multi-party computation(MPC), named VPDC-MPC, to achieve both functions simultaneously in smart grid based on fog computing. VPDC-MPC realizes verifiable secret sharing of users’ data and data aggregation without revealing individual reports via practical cryptosystem and verifiable secret sharing scheme. Besides, we propose an efficient algorithm for batch verification of share consistency and detection of error reports if the external adversaries modify the SMs’ report. Furthermore, VPDC-MPC allows both the control center and users with limited resources to obtain arbitrary arithmetic analysis (not only data aggregation) via secure multi-party computation between cloud servers in smart grid. Besides, VPDC-MPC tolerates fault of cloud servers and resists collusion. We also present security analysis and performance evaluation of our scheme, which indicates that even with tradeoff on computation and communication overhead, VPDC-MPC is practical with above features.

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References

  1. Arnold G. Challenges and opportunities in smart grid: aposition article. Proceedings of the IEEE, 2011, 99(6): 922–927

    Article  Google Scholar 

  2. Rehmani M H, Reisslein M, Rachedi A, Erol-Kantarci M, Radenkovic M. Guest editorial special section on smart grid and renewable energy resources: information and communication technologies with industry perspective. IEEE Transactions on Industrial Informatics, 2017, 13(6): 3119–3123

    Article  Google Scholar 

  3. Yousuf O, Mir R N. A survey on the internet of things security. Information and Computer Security, 2019, 27(2): 292–323

    Article  Google Scholar 

  4. Li X, Li J, Yiu S M, Gao C Z, Xiong J B. Privacy-preserving edge-assisted image retrieval and classification in IoT. Frontiers of Computer Science in China, 2019, 13(5): 1136–1147

    Article  Google Scholar 

  5. Sequeiros J, Chimuco F, Samaila M, Mário M F, Pedro R M I. Attack and system modeling applied to IoT, cloud, and mobile ecosystems: embedding security by design. ACM Computing Surveys, 2020, 53(2): 1–32

    Article  Google Scholar 

  6. Ozgur U, Tonyali S, Akkaya K, Senel F. Comparative evaluation of smart grid AMI networks: performance under privacy. In: Proceedings of IEEE Symposium on Computers and Communication. 2016, 1134–1136

  7. Wen M, Chen S, Lu R X, Li BB, Chen S J. Security and efficiency enhanced revocable access control for fog-based smart grid system. IEEE Access, 2019, 7: 137968–137981

    Article  Google Scholar 

  8. Shen X D, Zhu L H, Xu C, Sharif K, Lu R X. A privacy-preserving data aggregation scheme for dynamic groups in fog computing. Information Sciences, 2020, 514: 118–130

    Article  Google Scholar 

  9. Tariq N, Asim M, Al-Obeidat F, Farooqi M Z, Baker T, Hammoudeh M, Ghafir I. The security of big data in fog-enabled iot applications including blockchain: a survey. Sensors, 2019, 19(8): 1788

    Article  Google Scholar 

  10. Wu J, Ota K, Dong M X, Jianhua Li, Wang H K. Big data analysis-based security situational awareness for smart grid. IEEE Transactions on Big Data, 2018, 4(3): 408–417

    Article  Google Scholar 

  11. Zhou C X. Security analysis of a certi cateless public provable data possession scheme with privacy preserving for cloud-based smart grid data management system. International Journal of Network Security, 2020, 22(4): 584–588

    Google Scholar 

  12. Abidin A, Aly A, Cleemput S, Mustafa M. An MPC-based privacy-preserving protocol for a local electricity trading market. In: Proceedings of International Conference on Cryptology and Network Security. 2016, 615–625

  13. Knirsch F, Eibl G, Engel D. Error-resilient masking approaches for privacy preserving data aggregation. IEEE Transactions on Smart Grid, 2018, 9(4): 3351–3361

    Article  Google Scholar 

  14. Tonyali S, Cakmak O, Akkaya K, Mahmoud M M, Guvenç I. Secure data obfuscation scheme to enable privacy-preserving state estimation in smart grid AMI networks. IEEE Internet of Things Journal, 2016, 3(5): 709–719

    Article  Google Scholar 

  15. Garcia F D, Jacobs B. Privacy-friendly energy-metering via homomorphic encryption. In: Proceedings of the 6th International Workshop of Security and Trust Management. 2010, 226–238

  16. Liu Y N, Guo W, Fan C, Chang L, Cheng C. A practical privacy-preserving data aggregation (3PDA) scheme for smart grid. IEEE Transactions on Industrial Informatics, 2019, 15(3): 1767–1774

    Article  Google Scholar 

  17. Jo H J, Kim I S, Lee D H. Efficient and privacy-preserving metering protocols for smart grid systems. IEEE Transactions on Smart Grid, 2016, 7(3): 1732–1742

    Article  Google Scholar 

  18. Lu R X, Liang X H, Lin X D, Shen X. EPPA: an efficient and privacy-preserving aggregation scheme for secure smart grid communications. IEEE Transactions on Parallel and Distributed Systems, 2012, 23(9): 1621–1631

    Article  Google Scholar 

  19. He D, Kumar N, Zeadally S, Vinel A, Yang L. Efficient and privacy-preserving data aggregation scheme for smart grid against internal adversaries. IEEE Transactions on Smart Grid, 2017, 8(5): 2411–2419

    Article  Google Scholar 

  20. Abdallah A, Shen X. A lightweight lattice-based homomorphic privacy-preserving data aggregation scheme for smart grid. IEEE Transactions on Smart Grid, 2018, 9(1): 396–405

    Article  Google Scholar 

  21. Abdallah A, Shen X. Lightweight security and privacy preserving scheme for smart grid customer-side networks. IEEE Transactions on Smart Grid, 2017, 8(3): 1064–1074

    Article  Google Scholar 

  22. Yao A C. Protocols for secure computations (extended abstract). In: Proceedings of the 23rd Annual Symposium on Foundations of Computer Science. 1982, 160–164

  23. Ben-Or M, Goldwasser S, Wigderson A. Completeness theorems for non-cryptographic fault-tolerant distributed computation (extended abstract). In: Proceedings of the 20th Annual ACM Symposium on Theory of Computing. 1988, 1–10

  24. Gentry C. Fully homomorphic encryption using ideal lattices. In: Proceedings of the 41st Annual ACM Symposium on Theory of Computing. 2009, 169–178

  25. Chillotti I, Gama N, Georgieva M, Izabachène M. Faster packed homomorphic operations and efficient circuit bootstrapping for TFHE. In: Proceedings of the 23rd International Conference on the Theory and Applications of Cryptology and Information Security. 2017, 377–408

  26. Danezis G, Fournet C, Kohlweiss M, Béguelin S Z. Smart meter aggregation via secret-sharing. In: Proceedings of the 2013 ACM Workshop on Smart Energy Grid Security. 2013, 75–80

  27. Rottondi C, Verticale G, Krauss C. Distributed privacy-preserving aggregation of metering data in smart grids. IEEE Journal on Selected Areas in Communications, 2013, 31(7): 1342–1354

    Article  Google Scholar 

  28. Mustafa M A, Cleemput S, Aly A, Abidin A. A secure and privacy-preserving protocol for smart metering operational data collection. IEEE Transactions on Smart Grid, 2019, 10(6): 6481–6490

    Article  Google Scholar 

  29. Gamal T E. A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE Transactions on Information Theory, 1984, 31: 469–472

    MathSciNet  Google Scholar 

  30. Pedersen T P. Non-interactive and information-theoretic secure verifiable secret sharing. In: Proceedings of the 11th International Cryptology Conference. 1991, 129–140

  31. Paillier P. Public-key cryptosystems based on composite degree residuosity classes. In: Proceedings of the International Conference on the Theory and Application of Cryptographic Techniques. 1999, 223–238

  32. Chen L, Lu R, Cao Z. PDAFT: a privacy-preserving data aggregation scheme with fault tolerance for smart grid communications. Peer-to-Peer Networking and Applications, 2015, 8(6): 1122–1132

    Article  Google Scholar 

  33. Boneh D, Goh E J, Nissim K. Evaluating 2-dnf formulas on ciphertexts. In: Proceedings of the 2nd International Conference of Theory of Cryptography. 2005, 325–341

  34. Melchor C A, Castagnos G, Gaborit P. Lattice-based homomorphic encryption of vector spaces. In: Proceedings of 2008 IEEE International Symposium on Information Theory. 2008, 1858–1862

  35. Dong X L, Zhou J, Alharbi K, Lin X D, Cao Z F. An elgamal-based efficient and privacy-preserving data aggregation scheme for smart grid. In: Proceedings of IEEE Global Communications Conference. 2014, 4720–4725

  36. Shen H, Zhang M W, Shen J. Efficient privacy-preserving cube-data aggregation scheme for smart grids. IEEE Transactions on Information Forensics and Security, 2017, 12(6): 1369–1381

    Article  Google Scholar 

  37. Shamir A. How to share a secret. Communications of the ACM, 1979, 22(11): 612–613

    Article  MathSciNet  MATH  Google Scholar 

  38. Canetti R. Security and composition of multiparty cryptographic protocols. Journal of Cryptology, 2000, 13(1): 143–202

    Article  MathSciNet  MATH  Google Scholar 

  39. Liu J N, Weng J, Yang A J, Chen Y Z, Lin X D. Enabling efficient and privacy-preserving aggregation communication and function query for fog computing-based smart grid. IEEE Transaction on Smart Grid, 2020, 11(1): 247–257

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Key Research and Development Project of China (Grant No. 2020YFA0712300), and in part by the National Natural Science Foundation of China (Grant Nos. 62132005, 61632012, 62172162 and 62072404).

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Authors and Affiliations

  1. Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, 200062, China

    Zhusen Liu, Zhenfu Cao, Xiaolei Dong, Xiaopeng Zhao, Haiyong Bao & Jiachen Shen

  2. Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen, 518055, China

    Zhenfu Cao

  3. School of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, 310018, China

    Haiyong Bao

Authors
  1. Zhusen Liu
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  2. Zhenfu Cao
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  3. Xiaolei Dong
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  4. Xiaopeng Zhao
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  5. Haiyong Bao
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  6. Jiachen Shen
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Corresponding author

Correspondence to Zhenfu Cao.

Additional information

Zhusen Liu received his Master degree in Ploymer Chemistry and Physics from Zhejiang University, China in 2018. He is currently a PhD Candidate in Department of Cryptography and Cyber Security, School of Software Engineering at East China Normal University, China. His research interests include applied cryptography, network and information security, and IoT security.

Zhenfu Cao is currently a Distinguished Professor with East China Normal University, China. Since 1981, over 400 academic papers have been published in journals or conferences. His research interests mainly include number theory, cryptography, and information security. He serves as a member of the Expert Panel of the National Nature Science Fund of China. He has received a number of awards and the Leader of the Asia 3 Foresight Program (61161140320), and the key project (61033014,61632012) of the National Natural Science Foundation of China.

Xiaolei Dong is a Distinguished Professor in East China Normal University, China. Her primary research interests include number theory, cryptography, and trusted computing. She hosts a number of research projects supported by the National Basic Research Program of China (973 Program), and the special funds on information security of the National Development and Reform Commission, and the National Natural Science Foundation of China.

Xiaopeng Zhao received his Bachelor degree in computer science and technology from Northeastern University, China in 2014. He received the PhD degree in Department of Cryptography and Cyber Security, School of Software Engineering at East China Normal University, China in 2020. He is currently with the School of Computer Science and Technology, Donghua University, China. His research interests include public-key cryptography, computational number theory and algorithm.

Haiyong Bao received the PhD degree in computer science from Shanghai Jiao Tong University, China in 2006. Since February 2011, he has been an Associate Professor and a Full Professor with the School of Computer Science and Information Engineering, Zhejiang Gongshang University, and the School of Software Engineering, East China Normal University, China. From June 2014 to May 2015, he worked as a Postdoctoral Research Fellow at Nanyang Technological University, Singapore. Dr. Bao’s research interests include network and information security, applied cryptography, and big data security.

Jiachen Shen received his Bachelor degree at Shanghai Jiao Tong University, China in 2001, his Master and PhD degrees at University of Louisiana at Lafayette, USA in 2003 and 2008, respectively. He joined East China Normal University, China in 2015. His research interests include applied cryptography, cloud security, searchable encryption, and blockchains.

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Liu, Z., Cao, Z., Dong, X. et al. A verifiable privacy-preserving data collection scheme supporting multi-party computation in fog-based smart grid. Front. Comput. Sci. 16, 161810 (2022). https://doi.org/10.1007/s11704-021-0410-0

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