Novel Temporal Perturbation-Based Privacy-Preserving Mechanism for Smart Meters

Abstract

Smart meters provide strong data support for the intelligent construction of power grids but expose users’ privacy-sensitive information to adversaries. Thus, several mechanisms have been proposed for smart meter privacy protection. However, these existing mechanisms either lack consideration of the protection for customer power consumption mode or focus only on data security and ignore the data availability for intelligent services of smart meters. In this study, we propose a temporal perturbation-based privacy-preserving mechanism to achieve a balance between privacy security and data availability. The time disturbance model improves privacy security by staggering the acquisition and release time of smart meter data, destructs the load characteristics hidden in the power waveform, and realizes the fuzzification of real consumption events. Moreover, data availability for metering and billing, electronic scheduling and management, and value-added services is guaranteed. We analyze data availability and privacy security from theoretical and experimental perspectives, deduce a data availability analysis model, and introduce a non-intrusive load monitoring algorithm as a testing method for evaluating security performance. Evaluation results show that the proposed scheme performs well in protecting users’ consumption pattern and is resistant to attacks by load detection; it also ensures data availability when used for intelligent services.

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References

  1. 1.

    Fang X, Misra S, Xue G et al (2012) Smart grid — the new and improved power grid: a survey[J]. IEEE Commun Surv Tutor 14(4):944–980

    Article  Google Scholar 

  2. 2.

    Bayindir R, Hossain E, Vadi S (2016) The path of the smart grid -the new and improved power grid[C]// smart grid workshop and certificate program. IEEE :1–8

  3. 3.

    Kalogridis G, Cepeda R, Denic SZ et al (2011) ElecPrivacy: evaluating the privacy protection of electricity management algorithms[J]. IEEE Trans Smart Grid 2(4):750–758

    Article  Google Scholar 

  4. 4.

    Sankar L, Rajagopalan SR, Mohajer S et al (2013) Smart meter privacy: a theoretical framework[J]. IEEE Trans Smart Grid 4(2):837–846

    Article  Google Scholar 

  5. 5.

    Li X, Liang X, Lu R et al (2012) Securing smart grid: cyber attacks, countermeasures, and challenges[J]. Commun Mag IEEE 50(8):38–45

    Article  Google Scholar 

  6. 6.

    Asghar MR, Dán G, Miorandi D, et al. (2017) Smart meter data privacy: a survey[J]. IEEE Communications Surveys & Tutorials (99):1–1

  7. 7.

    Vuković O, Dán G, Bobba RB (2013) Confidentiality-preserving obfuscation for cloud-based power system contingency analysis[C]// IEEE international conference on smart grid communications. IEEE :432–437

  8. 8.

    Zhang Z, Qin Z, Zhu L, et al. (2017) Cost-friendly differential privacy for smart meters: exploiting the dual roles of the noise[J]. IEEE Transactions on Smart Grid (99):1–1

  9. 9.

    Kursawe K, Danezis G, Kohlweiss M (2011) Privacy-friendly aggregation for the smart-grid[C]// international conference on privacy enhancing technologies. Springer-Verlag :175–191

  10. 10.

    Varodayan D, Khisti A (2011) Smart meter privacy using a rechargeable battery: minimizing the rate of information leakage[C]// IEEE international conference on acoustics, speech and signal processing. IEEE :1932–1935

  11. 11.

    Li S, Khisti A, Mahajan A (2016) Privacy-optimal strategies for smart metering systems with a rechargeable battery[C]// American control conference. IEEE

  12. 12.

    Backes M, Meiser S (2014) Differentially private smart metering with battery recharging[M]// data privacy management and autonomous spontaneous security. Springer, Berlin Heidelberg, pp 194–212

    Google Scholar 

  13. 13.

    Dwork C, Mcsherry F, Nissim K (2006) Calibrating noise to sensitivity in private data analysis[M]// theory of cryptography. Springer, Berlin Heidelberg, pp 637–648

    Google Scholar 

  14. 14.

    Dwork C (2008) Differential privacy: a survey of results[C]// international conference on theory and applications of MODELS of computation. Springer-Verlag :1–19

  15. 15.

    Ni J, Zhang K, Alharbi K et al. (2017) Differentially private smart metering with fault tolerance and range-based filtering[J]. IEEE Transactions on Smart Grid (99):1–1

  16. 16.

    Zhao J, Jung T, Wang Y et al. (2014) Achieving differential privacy of data disclosure in the smart grid[C]// IEEE INFOCOM. IEEE, :504–512

  17. 17.

    Shuai L, Mengye L, Gaocheng L et al (2017) A novel distance metric: generalized relative entropy[J]. Entropy 19(6):269

    Article  Google Scholar 

  18. 18.

    Acs G, Castelluccia C (2011) I have a DREAM!(differentially private smartmetering)[C]//proceedings of the 13th international conference on information hiding.[s.l.]. Springer, Berlin Heidelberg, pp 118–132

    Google Scholar 

  19. 19.

    Paillier P (1999) Public-key cryptosystems based on composite degree residuosity classes[C]// advances in cryptology - EUROCRYPT '99, international conference on the theory and application of cryptographic techniques, Prague, Czech Republic, may 2-6, 1999, proceeding. DBLP :223–238

  20. 20.

    Lu R, Liang X, Li X et al (2012) EPPA: an efficient and privacy-preserving aggregation scheme for secure smart grid communications[J]. IEEE Trans Parallel Distrib Syst 23(9):1621–1631

    Article  Google Scholar 

  21. 21.

    Ruj S, Nayak A (2013) A decentralized security framework for data aggregation and access control in smart grids[J]. IEEE Trans Smart Grid 4(1):196–205

    Article  Google Scholar 

  22. 22.

    Lin HY, Tzeng WG, Shen ST et al. (2012) A practical smart metering system supporting privacy preserving billing and load monitoring[J]

  23. 23.

    Liu S, Z Pan WF, Cheng* X (2017) Fractal generation method based on asymptote family of generalized Mandelbrot set and its application [J]. J Nonlinear Sci Appl 10(3):1148–1161

    MathSciNet  Article  Google Scholar 

  24. 24.

    Liu S, Fu W, He L et al (2017) Distribution of primary additional errors in fractal encoding method[J]. Multimed Tools Appl 76(4):5787–5802

    Article  Google Scholar 

  25. 25.

    Beckel C, Kleiminger W, Cicchetti R, et al. (2014) The ECO data set and the performance of non-intrusive load monitoring algorithms[C]// ACM conference on embedded Systems for Energy-Efficient Buildings. ACM :80–89

  26. 26.

    Zhen X, Jinlong W, Guoru D et al. (2018) Device-to-device communications underlying UAV-supported social networking[J]. IEEE Access:1

  27. 27.

    Zhang Z, Guo X, Lin Y (2018) Trust management method of D2D communication based on RF fingerprint identification[J]. IEEE Access :1

  28. 28.

    Batra N, Kelly J, Parson O, et al. (2014) NILMTK:an open source toolkit for non-intrusive load monitoring[C]// international conference on future energy systems. ACM :265–276

  29. 29.

    Zeifman M, Roth K (2011) Nonintrusive appliance load monitoring: review and outlook[J]. IEEE Trans Consum Electron 57(1):76–84

    Article  Google Scholar 

  30. 30.

    Lin Y, Zhu X, Zheng Z, et al. (2017) The individual identification method of wireless device based on dimensionality reduction and machine learning[J]. J Supercomput (5):1–18

  31. 31.

    Tan O, Gunduz D, Poor HV (2013) Increasing smart meter privacy through energy harvesting and storage devices[J]. IEEE J Select Areas Commun 31(7):1331–1341

    Article  Google Scholar 

  32. 32.

    Fan L, Xiong L (2013) Adaptively sharing real-time aggregate with differential privacy. IEEE Trans Knowledge Data Eng (TKDE) 26(9):2094–2106

    Google Scholar 

  33. 33.

    Ma X, Wang T, Lin Y et al. (2018) Parallel iterative inter-carrier interference cancellation in underwater acoustic orthogonal frequency division multiplexing[J]. Wirel Pers Commun

  34. 34.

    Bhotto MZA, Makonin S, Bajic I (2017) Load disaggregation based on aided linear integer programming[J]. IEEE Trans Circ Syst II Express Briefs (99):1

  35. 35.

    Tu Y, Lin Y, Wang J et al (2015) Semi-supervised learning with generative adversarial networks on digital signal modulation classification[J]. Comput Mater Continua 55(2):243–254

    Google Scholar 

  36. 36.

    Yun L, Chao W, Jiaxing W et al (2016) A novel dynamic Spectrum access framework based on reinforcement learning for cognitive radio sensor networks[J]. Sensors 16(10):1675

    Article  Google Scholar 

  37. 37.

    Zoha A, Gluhak et al. (2012) Sensors, Vol. 12, Pages 16838–16866: Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey[J]

  38. 38.

    Weiss M, Helfenstein A, Mattern F et al Leveraging smart meter data to recognize home appliances[C]// IEEE international conference on pervasive computing and communications. IEEE 2012:190–197

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Acknowledgements

The research work reported in this paper is supported by the National Natural Science Foundation of China (41671443, 61701453), Fundamental Research Funds for the Central Universities (2042017kf0044), and LIESMARS Special Research Funding.

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Correspondence to Zhenquan Xu.

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Wang, X., Xu, Z., Cai, Z. et al. Novel Temporal Perturbation-Based Privacy-Preserving Mechanism for Smart Meters. Mobile Netw Appl 25, 1548–1562 (2020). https://doi.org/10.1007/s11036-019-01359-8

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Keywords

  • Smart meter
  • Privacy protection
  • Data availability
  • Time disturbance
  • Non-intrusive load monitoring