Anonimização de Dados de Trajetórias em Grupos para Disponibilização à Pesquisa Universitária
Resumo
A utilização de dispositivos móveis conectados à rede Wi-Fi de câmpus universitários permite a captura de dados de trajetória dos alunos. A união destes dados em conjunto com as informações dos alunos cadastradas nos sistemas das universidades torna viável a criação de trajetórias semânticas com quase-identificadores pessoais. No entanto, esses dados podem colocar a privacidade das pessoas em risco. Por essa razão, neste artigo é demonstrada uma nova técnica de anonimização de dados de trajetória chamada Mix β-k-anonymity. A técnica disponibiliza pontos espaço-temporais que formam um conjunto de possíveis trajetórias realizadas por grupos de pessoas com quase-identificador semelhante. O objetivo é aplicar este método para resolver o problema de privacidade de dados tornando a publicação destes possível. Os resultados deste trabalho demonstram que a comunidade acadêmica pode ter acesso a dados de qualidade para pesquisa operacional em mobilidade no campus onde a relevância demográfica é atendida com a manutenção da privacidade dos usuários.
Referências
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Alvares, L. O., Bogorny, V., Kuijpers, B., de Macedo, J. A. F., Moelans, B., and Vaisman, A. (2007). A model for enriching trajectories with semantic geographical information. In Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems, page 22. ACM.
Domingo-Ferrer, J. and Torra, V. (2001). Disclosure control methods and information loss for microdata. Confidentiality, disclosure, and data access: theory and practical applications for statistical agencies, pages 91–110.
Golle, P. and Partridge, K. (2009). On the anonymity of home/work location pairs. In International Conference on Pervasive Computing, pages 390–397. Springer.
Gramaglia, M., Fiore, M., Tarable, A., and Banchs, A. (2017). Preserving mobile subscriber privacy in open datasets of spatiotemporal trajectories. In INFOCOM 2017-IEEE Conference on Computer Communications, IEEE, pages 1–9. IEEE.
Hutchins, R. and Zegura, E. W. (2002). Measurements from a campus wireless network. In Communications, 2002. ICC 2002. IEEE International Conference on, volume 5, pages 3161–3167. IEEE.
Li, N., Li, T., and Venkatasubramanian, S. (2007). t-closeness: Privacy beyond k-anonymity and l-diversity. In Data Engineering, 2007. ICDE 2007. IEEE 23rd International Conference on, pages 106–115. IEEE.
Lu, Q., Wang, C., Xiong, Y., Xia, H., Huang, W., and Gong, X. (2017). Personalized privacy-preserving trajectory data publishing. Chinese Journal of Electronics, 26(2):285–291.
Ma, M., Zhao, K., Sui, K., Xu, L., Li, Y., and Pei, D. (2017). You can hide, but your periodic schedule can’t. In Quality of Service (IWQoS), 2017 IEEE/ACM 25th International Symposium on, pages 1–6. IEEE.
Machanavajjhala, A., Gehrke, J., Kifer, D., and Venkitasubramaniam, M. (2006). l-diversity: Privacy beyond k-anonymity. In Data Engineering, 2006. ICDE’06. Proceedings of the 22nd International Conference on, pages 24–24. IEEE.
Mahdavifar, S., Abadi, M., Kahani, M., and Mahdikhani, H. (2012). A clustering-based approach for personalized privacy preserving publication of moving object trajectory data. In International Conference on Network and System Security, pages 149–165.
Meyerson, A. and Williams, R. (2004). On the complexity of optimal k-anonymity. In Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pages 223–228. ACM.
Monreale, A., Trasarti, R., Pedreschi, D., Renso, C., and Bogorny, V. (2011). C-safety: a framework for the anonymization of semantic trajectories. Trans. Data Privacy, 4(2):73–101.
Nergiz, M. E., Atzori, M., and Saygin, Y. (2007). Perturbation-driven anonymization of trajectories. Technical report, Technical Report 2007-TR-017, ISTI-CNR, Pisa.
Nergiz, M. E., Atzori, M., and Saygin, Y. (2008). Towards trajectory anonymization: a generalization-based approach. In Proceedings of the SIGSPATIAL ACM GIS 2008 International Workshop on Security and Privacy in GIS and LBS, pages 52–61. ACM.
Rajesh, N. and Abraham, S. (2017). Privacy preserved approach for trajectory anonymization through zone creation for halting points. In Networks & Advances in Computational Technologies (NetACT), 2017 International Conference on, pages 229–234. IEEE.
Schwab, D. and Bunt, R. (2004). Characterising the use of a campus wireless network. In INFOCOM 2004. Twenty-third AnnualJoint Conference of the IEEE Computer and Communications Societies, volume 2, pages 862–870. IEEE.
Spaccapietra, S., Parent, C., Damiani, M. L., de Macedo, J. A., Porto, F., and Vangenot, C. (2008). A conceptual view on trajectories. Data & knowledge engineering, 65(1):126–146.
Sweeney, L. (2002). Achieving k-anonymity privacy protection using generalization and suppression. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05):571–588.
Tang, D. and Baker, M. (2000). Analysis of a local-area wireless network. In Proceedings of the 6th annual international conference on Mobile computing and networking, pages 1–10. ACM.
Terrovitis, M., Poulis, G., Mamoulis, N., and Skiadopoulos, S. (2017). Local suppression and splitting techniques for privacy preserving publication of trajectories. IEEE Transactions on Knowledge and Data Engineering, 29(7):1466–1479.
Wang, F., Zhu, X., and Miao, J. (2017). Semantic trajectories-based social relationships discovery using wifi monitors. Personal and Ubiquitous Computing, 21(1):85–96.
Aggarwal, G., Feder, T., Kenthapadi, K., Motwani, R., Panigrahy, R., Thomas, D., and Zhu, A. (2005). Anonymizing tables. In International Conference on Database Theory, pages 246–258. Springer.
Alvares, L. O., Bogorny, V., Kuijpers, B., de Macedo, J. A. F., Moelans, B., and Vaisman, A. (2007). A model for enriching trajectories with semantic geographical information. In Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems, page 22. ACM.
Domingo-Ferrer, J. and Torra, V. (2001). Disclosure control methods and information loss for microdata. Confidentiality, disclosure, and data access: theory and practical applications for statistical agencies, pages 91–110.
Golle, P. and Partridge, K. (2009). On the anonymity of home/work location pairs. In International Conference on Pervasive Computing, pages 390–397. Springer.
Gramaglia, M., Fiore, M., Tarable, A., and Banchs, A. (2017). Preserving mobile subscriber privacy in open datasets of spatiotemporal trajectories. In INFOCOM 2017-IEEE Conference on Computer Communications, IEEE, pages 1–9. IEEE.
Hutchins, R. and Zegura, E. W. (2002). Measurements from a campus wireless network. In Communications, 2002. ICC 2002. IEEE International Conference on, volume 5, pages 3161–3167. IEEE.
Li, N., Li, T., and Venkatasubramanian, S. (2007). t-closeness: Privacy beyond k-anonymity and l-diversity. In Data Engineering, 2007. ICDE 2007. IEEE 23rd International Conference on, pages 106–115. IEEE.
Lu, Q., Wang, C., Xiong, Y., Xia, H., Huang, W., and Gong, X. (2017). Personalized privacy-preserving trajectory data publishing. Chinese Journal of Electronics, 26(2):285–291.
Ma, M., Zhao, K., Sui, K., Xu, L., Li, Y., and Pei, D. (2017). You can hide, but your periodic schedule can’t. In Quality of Service (IWQoS), 2017 IEEE/ACM 25th International Symposium on, pages 1–6. IEEE.
Machanavajjhala, A., Gehrke, J., Kifer, D., and Venkitasubramaniam, M. (2006). l-diversity: Privacy beyond k-anonymity. In Data Engineering, 2006. ICDE’06. Proceedings of the 22nd International Conference on, pages 24–24. IEEE.
Mahdavifar, S., Abadi, M., Kahani, M., and Mahdikhani, H. (2012). A clustering-based approach for personalized privacy preserving publication of moving object trajectory data. In International Conference on Network and System Security, pages 149–165.
Meyerson, A. and Williams, R. (2004). On the complexity of optimal k-anonymity. In Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pages 223–228. ACM.
Monreale, A., Trasarti, R., Pedreschi, D., Renso, C., and Bogorny, V. (2011). C-safety: a framework for the anonymization of semantic trajectories. Trans. Data Privacy, 4(2):73–101.
Nergiz, M. E., Atzori, M., and Saygin, Y. (2007). Perturbation-driven anonymization of trajectories. Technical report, Technical Report 2007-TR-017, ISTI-CNR, Pisa.
Nergiz, M. E., Atzori, M., and Saygin, Y. (2008). Towards trajectory anonymization: a generalization-based approach. In Proceedings of the SIGSPATIAL ACM GIS 2008 International Workshop on Security and Privacy in GIS and LBS, pages 52–61. ACM.
Rajesh, N. and Abraham, S. (2017). Privacy preserved approach for trajectory anonymization through zone creation for halting points. In Networks & Advances in Computational Technologies (NetACT), 2017 International Conference on, pages 229–234. IEEE.
Schwab, D. and Bunt, R. (2004). Characterising the use of a campus wireless network. In INFOCOM 2004. Twenty-third AnnualJoint Conference of the IEEE Computer and Communications Societies, volume 2, pages 862–870. IEEE.
Spaccapietra, S., Parent, C., Damiani, M. L., de Macedo, J. A., Porto, F., and Vangenot, C. (2008). A conceptual view on trajectories. Data & knowledge engineering, 65(1):126–146.
Sweeney, L. (2002). Achieving k-anonymity privacy protection using generalization and suppression. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05):571–588.
Tang, D. and Baker, M. (2000). Analysis of a local-area wireless network. In Proceedings of the 6th annual international conference on Mobile computing and networking, pages 1–10. ACM.
Terrovitis, M., Poulis, G., Mamoulis, N., and Skiadopoulos, S. (2017). Local suppression and splitting techniques for privacy preserving publication of trajectories. IEEE Transactions on Knowledge and Data Engineering, 29(7):1466–1479.
Wang, F., Zhu, X., and Miao, J. (2017). Semantic trajectories-based social relationships discovery using wifi monitors. Personal and Ubiquitous Computing, 21(1):85–96.
Publicado
25/10/2018
Como Citar
GOMES, Fernanda O.; BALDISSERA, Julia; AGOSTINHO, Bruno M.; SIMÕES, Douglas; MARTINA, Jean E..
Anonimização de Dados de Trajetórias em Grupos para Disponibilização à Pesquisa Universitária. In: SIMPÓSIO BRASILEIRO DE CIBERSEGURANÇA (SBSEG), 18. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 71-84.
DOI: https://doi.org/10.5753/sbseg.2018.4244.
