Privacy-Preserving Techniques for Social Network Analysis

  • André L. C. Mendonça Universidade Federal do Ceará (UFC)
  • Felipe T. Brito Universidade Federal do Ceará (UFC)
  • Javam C. Machado Universidade Federal do Ceará (UFC)


With the increasing concerns over data privacy, preserving the privacy of individuals in social network analysis has become crucial. This tutorial provides a comprehensive overview of methods and techniques to protect individual privacy while conducting social network analysis. We perform a deep analysis of differential privacy, which is a rigorous mathematical framework to protect individual privacy while enabling accurate analysis of network structure and characteristics. Additionally, this tutorial explores a variety of examples and case studies to demonstrate the application of these techniques in practical scenarios.
Palavras-chave: Differential Privacy, Social Network Analysis


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.

Brito, F. T., Farias, V. A., Flynn, C., Majumdar, S., Machado, J. C., and Srivastava, D. (2023). Global and local differentially private release of count-weighted graphs. Proceedings of the ACM on Management of Data, 1(2):1–25.

Dwork, C. (2006). Differential privacy. In International colloquium on automata, languages, and programming, pages 1–12. Springer.

Farias, V. A., Brito, F. T., Flynn, C., Machado, J. C., Majumdar, S., and Srivastava, D. (2023). Local dampening: Differential privacy for non-numeric queries via local sensitivity. The VLDB Journal, pages 1–24.

Hay, M., Li, C., Miklau, G., and Jensen, D. (2009). Accurate estimation of the degree distribution of private networks. In International Conference on Data Mining, pages 169–178. IEEE.

Kasiviswanathan, S. P., Nissim, K., Raskhodnikova, S., and Smith, A. (2013). Analyzing graphs with node differential privacy. In Theory of Cryptography Conference, pages 457–476. Springer.

Kearns, M. and Roth, A. (2019). The ethical algorithm: The science of socially aware algorithm design. Oxford University Press.

Knoke, D. and Yang, S. (2019). Social network analysis. SAGE publications.

Silva, R. R. C., Leal, B. C., Brito, F. T., Vidal, V. M., and Machado, J. C. (2017). A differentially private approach for querying rdf data of social networks. In International Database Engineering & Applications Symposium, pages 74–81.

Xia, S., Chang, B., Knopf, K., He, Y., Tao, Y., and He, X. (2021). Dpgraph: A benchmark platform for differentially private graph analysis. In International Conference on Management of Data, pages 2808–2812.
MENDONÇA, André L. C.; BRITO, Felipe T.; MACHADO, Javam C.. Privacy-Preserving Techniques for Social Network Analysis. In: TUTORIAIS - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 38. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 174-178. DOI: