Compartilhamento de Dados de Tráfego de Rede Utilizando Privacidade Diferencial
Resumo
Dados de tráfego de redes são úteis para uma variedade de aplicações. Geralmente as entidades que coletam esse tipo de dado, por exemplo provedores de internet (ISPs), compartilham suas informações de tráfego de rede com entidades externas. Contudo, esse compartilhamento pode levar a violações de privacidade dos indivíduos contidos nesses dados. Este trabalho propõe uma nova abordagem para compartilhamento de dados de tráfego de rede utilizando privacidade diferencial, um método que tem como objetivo adicionar ruído sobre os dados originais. Resultados experimentais mostram que a abordagem proposta introduz menos ruído nos dados quando comparada a outras técnicas que também adotam privacidade diferencial.
Referências
Brito, F. T. and Machado, J. C. (2017). Preservação de privacidade de dados: Fundamentos, técnicas e aplicações. Jornadas de atualização em informática, pages 91–130.
Bureau, U. C. (2021). Census bureau sets key parameters to protect privacy in 2020 census results. [link]. Acesso 03 de fevereiro de 2023.
Comissão Federal de Comunicação (2018). Privacidade do cliente. [link]. Acesso 17 de fevereiro de 2023.
de Castro Vidal, I., da Costa Mendonça, A. L., Rousseau, F., and Machado, J. C. (2020). Protecting: An application of local differential privacy for iot at the edge in smart home scenarios. In Anais do XXXVIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 547–560. SBC.
Dwork, C. (2006). Differential privacy. In International Colloquium on Automata, Languages, and Programming, pages 1–12. Springer.
GDPR (2018). General data protection regulation. https://gdpr-info.eu/. Acesso 17 de fevereiro de 2023.
Ghosh, A., Roughgarden, T., and Sundararajan, M. (2009). Universally utility-maximizing privacy mechanisms. In Proceedings of the forty-first annual ACM symposium on Theory of computing, pages 351–360.
IANA (2023). Internet Assigned Numbers Authority. https://www.iana.org/. Acesso em: 24 de março de 2023.
Joshi, M. and Hadi, T. H. (2015). A review of network traffic analysis and prediction techniques. arXiv preprint arXiv:1507.05722.
Koufogiannis, F. and Pappas, G. J. (2017). Diffusing private data over networks. IEEE Transactions on Control of Network Systems, 5(3):1027–1037.
Le Ny, J. and Pappas, G. J. (2013). Privacy-preserving release of aggregate dynamic models. In Proceedings of the 2nd ACM international conference on High confidence networked systems, pages 49–56.
LGPD (2019). Lei geral de proteção de dados pessoais. [link]. Acesso 17 de fevereiro de 2023.
Lu, Y., Tian, H., Shen, H., and Xu, D. (2018). Privacy preserving classification based on perturbation for network traffic. In Parallel and Distributed Computing, Applications and Technologies: 19th International Conference, PDCAT 2018, Jeju Island, South Korea, August 20-22, 2018, Revised Selected Papers 19, pages 121–132. Springer.
Machanavajjhala, A., He, X., and Hay, M. (2017). Differential privacy in the wild: A tutorial on current practices & open challenges. In Proceedings of the 2017 ACM International Conference on Management of Data, pages 1727–1730.
McSherry, F. D. (2009). Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data, pages 19–30.
Mirimir (2018). Collection of User Data by ISPs and Telecom Providers, and Sharing with Third Parties. [link]. Acesso em: 28 de março de 2023.
Sangeetha, S. and Sudha Sadasivam, G. (2019). Privacy of big data: a review. Handbook of big data and iot security, pages 5–23.
Shafiq, M., Yu, X., Laghari, A. A., Yao, L., Karn, N. K., and Abdessamia, F. (2016). Network traffic classification techniques and comparative analysis using machine learning algorithms. In 2016 2nd IEEE International Conference on Computer and Communications (ICCC), pages 2451–2455. IEEE.
Tanenbaum, A. S. and Wetherall, D. (2021). Network Computer 6th Edition. Pearson.
Zhang, J. et al. (2017). Privbayes: Private data release via bayesian networks. ACM Transactions on Database Systems (TODS), 42(4):1–41.
Zhang, X., Hamm, J., Reiter, M. K., and Zhang, Y. (2022). Defeating traffic analysis via differential privacy: a case study on streaming traffic. International Journal of Information Security, 21(3):689–706.
Bureau, U. C. (2021). Census bureau sets key parameters to protect privacy in 2020 census results. [link]. Acesso 03 de fevereiro de 2023.
Comissão Federal de Comunicação (2018). Privacidade do cliente. [link]. Acesso 17 de fevereiro de 2023.
de Castro Vidal, I., da Costa Mendonça, A. L., Rousseau, F., and Machado, J. C. (2020). Protecting: An application of local differential privacy for iot at the edge in smart home scenarios. In Anais do XXXVIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 547–560. SBC.
Dwork, C. (2006). Differential privacy. In International Colloquium on Automata, Languages, and Programming, pages 1–12. Springer.
GDPR (2018). General data protection regulation. https://gdpr-info.eu/. Acesso 17 de fevereiro de 2023.
Ghosh, A., Roughgarden, T., and Sundararajan, M. (2009). Universally utility-maximizing privacy mechanisms. In Proceedings of the forty-first annual ACM symposium on Theory of computing, pages 351–360.
IANA (2023). Internet Assigned Numbers Authority. https://www.iana.org/. Acesso em: 24 de março de 2023.
Joshi, M. and Hadi, T. H. (2015). A review of network traffic analysis and prediction techniques. arXiv preprint arXiv:1507.05722.
Koufogiannis, F. and Pappas, G. J. (2017). Diffusing private data over networks. IEEE Transactions on Control of Network Systems, 5(3):1027–1037.
Le Ny, J. and Pappas, G. J. (2013). Privacy-preserving release of aggregate dynamic models. In Proceedings of the 2nd ACM international conference on High confidence networked systems, pages 49–56.
LGPD (2019). Lei geral de proteção de dados pessoais. [link]. Acesso 17 de fevereiro de 2023.
Lu, Y., Tian, H., Shen, H., and Xu, D. (2018). Privacy preserving classification based on perturbation for network traffic. In Parallel and Distributed Computing, Applications and Technologies: 19th International Conference, PDCAT 2018, Jeju Island, South Korea, August 20-22, 2018, Revised Selected Papers 19, pages 121–132. Springer.
Machanavajjhala, A., He, X., and Hay, M. (2017). Differential privacy in the wild: A tutorial on current practices & open challenges. In Proceedings of the 2017 ACM International Conference on Management of Data, pages 1727–1730.
McSherry, F. D. (2009). Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data, pages 19–30.
Mirimir (2018). Collection of User Data by ISPs and Telecom Providers, and Sharing with Third Parties. [link]. Acesso em: 28 de março de 2023.
Sangeetha, S. and Sudha Sadasivam, G. (2019). Privacy of big data: a review. Handbook of big data and iot security, pages 5–23.
Shafiq, M., Yu, X., Laghari, A. A., Yao, L., Karn, N. K., and Abdessamia, F. (2016). Network traffic classification techniques and comparative analysis using machine learning algorithms. In 2016 2nd IEEE International Conference on Computer and Communications (ICCC), pages 2451–2455. IEEE.
Tanenbaum, A. S. and Wetherall, D. (2021). Network Computer 6th Edition. Pearson.
Zhang, J. et al. (2017). Privbayes: Private data release via bayesian networks. ACM Transactions on Database Systems (TODS), 42(4):1–41.
Zhang, X., Hamm, J., Reiter, M. K., and Zhang, Y. (2022). Defeating traffic analysis via differential privacy: a case study on streaming traffic. International Journal of Information Security, 21(3):689–706.
Publicado
06/08/2023
Como Citar
MONTEIRO, Felipe C.; BRITO, Felipe T.; CHAVES, Iago C.; MACHADO, Javam C..
Compartilhamento de Dados de Tráfego de Rede Utilizando Privacidade Diferencial. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 50. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 296-307.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2023.230739.
