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Data Privacy

  • Synthetic generation of spatial graphs. This software corresponds to the one prepared for the following paper:
    • Torra, V., Jonsson, A., Navarro-Arribas, G., Salas, J. (2018) Synthetic generation of spatial graphs, Int. J. of Intel. Systems, in press. (Published in open access here)
  • SoftwarePPDM.zip
    This software can be used to compute the disclosure risk and the information loss measures. In the .zip file, there are two .jar (java) files, one for numerical databases and the other for categorical databases. There is also an example file. This program is according to (and has been used with) with at least the following papers:
    • Nin, J., Herranz, J., Torra, V. (2008) Towards a More Realistic Disclosure Risk Assessment.In Privacy in Statistical Databases (PSD), volume 5262 of Lecture Notes in Computer Science, pages 152-165. Springer. PDF@Springer
    • Domingo-Ferrer, J., Torra, V., (2001) Disclosure control methods and information loss for microdata, Confidentiality, disclosure, and data access : Theory and practical applications for statistical agencies, Doyle, P., Lane, J.I., Theeuwes, J.J.M., Zayatz, L.V. eds., Elsevier, pp. 91-110.PDF@URV
    • Mateo-Sanz, J.M., Domingo-Ferrer, J., Sebé, F. Probabilistic information loss measures in confidentiality protection of continuous microdata, Data Mining and Knowledge Discovery, Vol. 11, pp. 181-193. Sep 2005. ISSN: 1384-5810.PDF@URV
    • Domingo-Ferrer, J., Torra, V., (2001) A quantitative comparison of disclosure control methods for microdata, Confidentiality, disclosure, and data access : Theory and practical applications for statistical agencies. Doyle, P.; Lane, J.I.; Theeuwes, J.J.M.; Zayatz, L.V. eds., Elsevier, pp. 111-133. PDF@URV
  • SDCmicro
    Statistical Disclosure Control methods for the generation of public- and scientific-use files in R (R-project)
    A description of this package can be found at:
    • Templ, M. (2008) Statistical Disclosure Control for Microdata Using the R-Package sdcMicro, Transactions on Data Privacy 1:2 67 - 85. PDF@TDP
  • Other code in R
    Implementation in R corresponding to the following chapter. Examples and disclosure risk evaluation (using a supervised approach as in machine learning) is given.
    • Abril, D., Navarro-Arribas, G., Torra, V. (2015) Data Privacy with R, in G. Navarro-Arribas, V. Torra (eds) Advanced research in data privacy, Springer, 63-82. PDF@Springer

Journals: Some other privacy-related journals (on Computer Science/Data Mining and Statistics)
Links to researchers and groups: