MSDA: multi-subset data aggregation scheme without trusted third party

Abstract

Data aggregation has been widely researched to address the privacy concern when data is published, meanwhile, data aggregation only obtains the sum or average in an area. In reality, more fine-grained data brings more value for data consumers, such as more accurate management, dynamic priceadjusting in the grid system, etc. In this paper, a multi-subset data aggregation scheme for the smart grid is proposed without a trusted third party, in which the control center collects the number of users in different subsets, and obtains the sum of electricity consumption in each subset, meantime individual user’s data privacy is still preserved. In addition, the dynamic and flexible user management mechanism is guaranteed with the secret key negotiation process among users. The analysis shows MSDA not only protects users’ privacy to resist various attacks but also achieves more functionality such as multi-subset aggregation, no reliance on any trusted third party, dynamicity. And performance evaluation demonstrates that MSDA is efficient and practical in terms of communication and computation overhead.

Keywords

multi-subset data aggregation / privacy-preserving / smart gird / dynamic user management

References

[1] Al-Fuqaha A, Guizani M, Mohammadi M, Aledhari M, Ayyash M. Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Communications Surveys and Tutorials, 2015, 17(4): 2347–2376

[2] Saleem A, Khan A, Malik S U R, Pervaiz H, Malik H, Alam M, Jindal A. Fesda: fog-enabled secure data aggregation in smart grid IoT network. IEEE Internet of Things Journal, 2020, 7(7): 6132–6142

[3] Liu Y, Wang Y,Wang X, Xia Z, Xu J. Privacy-preserving raw data collection without a trusted authority for IoT. Computer Networks, 2019, 148: 340–348

[4] Fang X, Misra S, Xue G, Yang D. Smart grid–the new and improved power grid: a survey. IEEE Communications Surveys and Tutorials, 2012, 14(4): 944–980

[5] Xue K, Zhu B, Yang Q, Wei D S L, Guizani M. An efficient and robust data aggregation scheme without a trusted authority for smart grid. IEEE Internet of Things Journal, 2020, 7(3): 1949–1959

[6] Song J, Liu Y, Shao J, Tang C. A dynamic membership data aggregation (DMDA) protocol for smart grid. IEEE Systems Journal, 2020, 14(1): 900–908

[7] Xue Q, Zhu Y,Wang J. Joint distribution estimation and naive bayes classification under local differential privacy. IEEE Transactions on Emerging Topics in Computing, 2019, DOI: 10.1109/TETC.2019.2959581

[8] Li X, Zhu Y, Wang J. Highly efficient privacy preserving locationbased services with enhanced one-round blind filter. IEEE Transactions on Emerging Topics in Computing, 2019, DOI: 10. 1109/TETC.2019. 2926385

[9] Li S, Xue K, Wei D S L, Yue H, Yu N, Hong P. SecGrid: a secure and efficient SGX-enabled smart grid system with rich functionalities. IEEE Transactions on Information Forensics and Security, 2020, 15:1318–1330

[10] Jia W, Zhu H, Cao Z, Dong X, Xiao C. Human-factor-aware privacypreserving aggregation in smart grid. IEEE Systems Journal, 2014, 8(2): 598–607

[11] Li X, Liu S, Wu F, Kumari S, Rodrigues J J P C. Privacy preserving data aggregation scheme for mobile edge computing assisted IoT applications. IEEE Internet of Things Journal, 2019, 6(3): 4755–4763

[12] Lu R, Heung K, Lashkari A H, Ghorbani A A. A lightweight privacypreserving data aggregation scheme for fog computing-enhanced IoT. IEEE Access, 2017, 5: 3302–3312

[13] Lu R, Liang X, Li X, Lin X, Shen X. EPPA: an efficient and privacypreserving aggregation scheme for secure smart grid communications. IEEE Transactions on Parallel and Distributed Systems, 2012, 23(9): 1621–1631

[14] Liu Y, Guo W, Fan C I, 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

[15] Acar A, Aksu H, Uluagac A S, Conti M. A survey on homomorphic encryption schemes: theory and implementation. ACM Computing Surveys (CSUR), 2018, 51(4): 79

[16] Lu R, Alharbi K, Lin X, Huang C. A novel privacy-preserving set aggregation scheme for smart grid communications. In: Proceedings of 2015 IEEE Global Communications Conference. 2015, 1–6

[17] Li S, Xue K, Yang Q, Hong P. PPMA: privacy preserving multisubset data aggregation in smart grid. IEEE Transactions on Industrial Informatics, 2018, 14(2): 462–471

[18] Gong X, Hua Q, Qian L, Yu D, Jin H. Communication efficient and privacy-preserving data aggregation without trusted authority. In: Proceedings of 2018 IEEE Conference on Computer Communications. 2018, 1250–1258

[19] Eibl G, Engel D. Differential privacy for real smart metering data. Computer Science-Research and Development, 2017, 32(1–2): 173–182

[20] Xue Q, Zhu Y, Wang J. Meanestimation over numeric data with personalized local differential privacy, Frontiers of Computer Science, 2020, DOI: 10.1007/s11704–020–0103–0

[21] Jia W, Zhu H, Cao Z, Dong X, Xiao C. Human-factor-aware privacypreserving aggregation in smart grid. IEEE Systems Journal, 2014, 8(2): 598–607

[22] Liu Y, Liu G, Cheng C, Xia Z, Shen J. A privacy-preserving health data aggregation scheme. KSII Transactions on Internet & Information Systems, 2016, 10(8): 3852–3864

[23] Lyu L, Nandakumar K, Rubinstein B, Jin J, Bedo J, Palaniswami M. PPFA: privacy preserving fog-enabled aggregation in smart grid. IEEE Transactions on Industrial Informatics, 2018, 14(8): 3733–3744

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

[25] Guan Z, Zhang Y, Wu L, Wu J, Li J, Ma Y, Hu J. APPA: an anonymous and privacy preserving data aggregation scheme for fog-enhanced IoT. Journal of Network and Computer Applications, 2019, 125: 82–92

[26] Guan Z, Zhang Y, Zhu L,Wu L, Yu S. Effect: an efficient flexible privacypreserving data aggregation scheme with authentication in smart grid. Science China Information Sciences, 2019, 62(3): 32103

[27] Zhang Y, Zhao J, Dong Z, Deng K, Ren F, Zheng X, Shu J. Privacypreserving data aggregation against false data injection attacks in fog computing. Sensors, 2018, 18(8): 2659

[28] Abdallah A, Shen X S. A lightweight lattice-based homomorphic privacypreserving data aggregation scheme for smart grid. IEEE Transactions on Smart Grid, 2016, 9(1): 396–405

[29] Huang D, Xue R, Liu F, Peng J, Zhao Z, Ji D. Formal verification of HMQV using ASM-SPV. In: Proceedings of International Conference on Intelligent Information Hiding and Multimedia Signal Processing. 2010, 486–489

[30] Krawczyk H. HMQV: a high-performance secure Diffie-Hellman protocol. In: Proceedings of Annual International Cryptology Conference. 2005, 546–566

RIGHTS & PERMISSIONS

Higher Education Press