Week 9 - Sensitive data

Learning goals

  • Distinguish between direct and indirect identifiers and explain their role in privacy risk (cont’d)
  • Understand the applications of k-anonymity and l-diversity for protecting sensitive data
  • Apply remediation techniques using programmatic approaches

Slides

Lab Files

Resources

  1. sdcMicro Documentation: https://sdcpractice.readthedocs.io/en/latest/intro.html

  2. sdcMicro Shiny app: https://sdcappdocs.readthedocs.io/en/latest/introsdcApp.html

Other useful links can be found on the slides.

Suggested readings

  1. Bledsoe, E. K., Burant, J. B., Higino, G. T., Roche, D. G., Binning, S. A., Finlay, K., … & Srivastava, D. S. (2022). Data rescue: saving environmental data from extinction. Proceedings of the Royal Society B, 289(1979), https://doi.org/10.1098/rspb.2022.0938

  2. Bourgault, B., Tremblay, H.; Schloss, I.R.; Plante, S. & Archambault, P. (2017). “Commercially Sensitive” Environmental Data: A Case Study of Oil Seep Claims for the Old Harry Prospect in the Gulf of St. Lawrence, Canada. Case Studies in the Environment. https://doi.org/10.1525/cse.2017.sc.454841

  3. Gehrke, J., Kifer, D., Machanavajjhala, A. (2011). ℓ-Diversity. In: van Tilborg, H.C.A., Jajodia, S. (eds) Encyclopedia of Cryptography and Security. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-5906-5_899

  4. Samarati, P., & Sweeney, L. (1998). Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. https://dataprivacylab.org/dataprivacy/projects/kanonymity/paper3.pdf

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