Um Mecanismo de Preservação de Privacidade para Medição Inteligente contra Empresa Curiosa
Resumo
As redes elétricas inteligentes permitem a comunicação entre clientes e a empresa de energia; entretanto, os dados de medição podem comprometer a privacidade dos usuários. A literatura propõe soluções para lidar com atacantes externos, mas não resolve o problema de ataques internos. Este artigo propõe um mecanismo de medição inteligente para proteger a privacidade contra empresas curiosas, utilizando assinaturas às cegas e agregação descentralizada. Na avaliação de desempenho, a solução apresentou uma redução de 44% no uso de CPU em comparação ao modelo parcialmente homomórfico e 46% em relação ao modelo com Shamir’s Secret Sharing, demonstrando um melhor equilíbrio entre privacidade e desempenho.
Palavras-chave:
Empresa curiosa, Privacidade, Segurança Cibernética, Assinatura às Cegas, Medição Inteligente
Referências
Ahir, R. K. and Chakraborty, B. (2022). A novel cluster-specific analysis framework for demand-side management and net metering using smart meter data. Sustainable Energy, Grids and Networks, 31:100771.
Asghar, M. R., Dán, G., Miorandi, D., and Chlamtac, I. (2017). Smart meter data privacy: A survey. IEEE Communications Surveys & Tutorials, 19(4):2820–2835.
Devlin, M. A. and Hayes, B. P. (2019). Non-intrusive load monitoring and classification of activities of daily living using residential smart meter data. IEEE Transactions on Consumer Electronics, 65(3):339–348.
Eibl, G. and Engel, D. (2014). Influence of data granularity on smart meter privacy. IEEE Transactions on Smart Grid, 6(2):930–939.
Finster, S. and Baumgart, I. (2013). Pseudonymous smart metering without a trusted third party. In 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, pages 1723–1728. IEEE.
Finster, S. and Baumgart, I. (2015). Privacy-aware smart metering: A survey. IEEE Communications Surveys & Tutorials, 17(2):1088–1101.
Joshi, S., Li, R., Bhattacharjee, S., Das, S. K., and Yamana, H. (2022). Privacy-preserving data falsification detection in smart grids using elliptic curve cryptography and homomorphic encryption. In 2022 IEEE International Conference on Smart Computing (SMARTCOMP), pages 229–234. IEEE.
Junior, W. L. R., Borges, F. A., Veloso, A. F. d. S., de AL Rabêlo, R., and Rodrigues, J. J. (2019). Low voltage smart meter for monitoring of power quality disturbances applied in smart grid. Measurement, 147:106890.
Kua, J., Hossain, M. B., Natgunanathan, I., and Xiang, Y. (2023). Privacy preservation in smart meters: current status, challenges and future directions. Sensors, 23(7):3697.
Lazzari, F., Mor, G., Cipriano, J., Gabaldon, E., Grillone, B., Chemisana, D., and Solsona, F. (2022). User behaviour models to forecast electricity consumption of residential customers based on smart metering data. Energy Reports, 8:3680–3691.
Li, K., Yang, Y., Wang, S., Shi, R., and Li, J. (2021). A lightweight privacy-preserving and sharing scheme with dual-blockchain for intelligent pricing system of smart grid. Computers & Security, 103:102189.
Rottondi, C., Savi, M., Polenghi, D., Verticale, G., and Krauß, C. (2012). Implementation of a protocol for secure distributed aggregation of smart metering data. In 2012 International Conference on Smart Grid Technology, Economics and Policies (SG-TEP), pages 1–4. IEEE.
Singh, P., Masud, M., Hossain, M. S., and Kaur, A. (2021). Blockchain and homomorphic encryption-based privacy-preserving data aggregation model in smart grid. Computers & Electrical Engineering, 93:107209.
Sultan, S. (2019). Privacy-preserving metering in smart grid for billing, operational metering, and incentive-based schemes: A survey. Computers & Security, 84:148–165.
Tonyali, S., Akkaya, K., Saputro, N., Uluagac, A. S., and Nojoumian, M. (2018). Privacy-preserving protocols for secure and reliable data aggregation in IoT-enabled smart metering systems. Future Generation Computer Systems, 78:547–557.
Tonyali, S., Saputro, N., and Akkaya, K. (2015). Assessing the feasibility of fully homomorphic encryption for smart grid AMI networks. In 2015 Seventh International Conference on Ubiquitous and Future Networks, pages 591–596. IEEE.
Van Aubel, P. and Poll, E. (2019). Smart metering in the Netherlands: What, how, and why. International Journal of Electrical Power & Energy Systems, 109:719–725.
Zhang, S., Zhang, Y., and Wang, B. (2023). Antiquantum privacy protection scheme in advanced metering infrastructure of smart grid based on consortium blockchain and RLWE. IEEE Systems Journal.
Asghar, M. R., Dán, G., Miorandi, D., and Chlamtac, I. (2017). Smart meter data privacy: A survey. IEEE Communications Surveys & Tutorials, 19(4):2820–2835.
Devlin, M. A. and Hayes, B. P. (2019). Non-intrusive load monitoring and classification of activities of daily living using residential smart meter data. IEEE Transactions on Consumer Electronics, 65(3):339–348.
Eibl, G. and Engel, D. (2014). Influence of data granularity on smart meter privacy. IEEE Transactions on Smart Grid, 6(2):930–939.
Finster, S. and Baumgart, I. (2013). Pseudonymous smart metering without a trusted third party. In 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, pages 1723–1728. IEEE.
Finster, S. and Baumgart, I. (2015). Privacy-aware smart metering: A survey. IEEE Communications Surveys & Tutorials, 17(2):1088–1101.
Joshi, S., Li, R., Bhattacharjee, S., Das, S. K., and Yamana, H. (2022). Privacy-preserving data falsification detection in smart grids using elliptic curve cryptography and homomorphic encryption. In 2022 IEEE International Conference on Smart Computing (SMARTCOMP), pages 229–234. IEEE.
Junior, W. L. R., Borges, F. A., Veloso, A. F. d. S., de AL Rabêlo, R., and Rodrigues, J. J. (2019). Low voltage smart meter for monitoring of power quality disturbances applied in smart grid. Measurement, 147:106890.
Kua, J., Hossain, M. B., Natgunanathan, I., and Xiang, Y. (2023). Privacy preservation in smart meters: current status, challenges and future directions. Sensors, 23(7):3697.
Lazzari, F., Mor, G., Cipriano, J., Gabaldon, E., Grillone, B., Chemisana, D., and Solsona, F. (2022). User behaviour models to forecast electricity consumption of residential customers based on smart metering data. Energy Reports, 8:3680–3691.
Li, K., Yang, Y., Wang, S., Shi, R., and Li, J. (2021). A lightweight privacy-preserving and sharing scheme with dual-blockchain for intelligent pricing system of smart grid. Computers & Security, 103:102189.
Rottondi, C., Savi, M., Polenghi, D., Verticale, G., and Krauß, C. (2012). Implementation of a protocol for secure distributed aggregation of smart metering data. In 2012 International Conference on Smart Grid Technology, Economics and Policies (SG-TEP), pages 1–4. IEEE.
Singh, P., Masud, M., Hossain, M. S., and Kaur, A. (2021). Blockchain and homomorphic encryption-based privacy-preserving data aggregation model in smart grid. Computers & Electrical Engineering, 93:107209.
Sultan, S. (2019). Privacy-preserving metering in smart grid for billing, operational metering, and incentive-based schemes: A survey. Computers & Security, 84:148–165.
Tonyali, S., Akkaya, K., Saputro, N., Uluagac, A. S., and Nojoumian, M. (2018). Privacy-preserving protocols for secure and reliable data aggregation in IoT-enabled smart metering systems. Future Generation Computer Systems, 78:547–557.
Tonyali, S., Saputro, N., and Akkaya, K. (2015). Assessing the feasibility of fully homomorphic encryption for smart grid AMI networks. In 2015 Seventh International Conference on Ubiquitous and Future Networks, pages 591–596. IEEE.
Van Aubel, P. and Poll, E. (2019). Smart metering in the Netherlands: What, how, and why. International Journal of Electrical Power & Energy Systems, 109:719–725.
Zhang, S., Zhang, Y., and Wang, B. (2023). Antiquantum privacy protection scheme in advanced metering infrastructure of smart grid based on consortium blockchain and RLWE. IEEE Systems Journal.
Publicado
19/05/2025
Como Citar
CASTRO, Tiago B.; CUNHA NETO, Helio N.; FERNANDES, Natalia C..
Um Mecanismo de Preservação de Privacidade para Medição Inteligente contra Empresa Curiosa. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 43. , 2025, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 210-223.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2025.5886.
