Abstract
Many of the potentially sensitive personal data produced and compiled in electronic sources are nominal and multi-attribute (e.g., personal interests, healthcare diagnoses, commercial transactions, etc.). For such data, which are discrete, finite and non-ordinal, privacy-protection methods should mask original values to prevent disclosure while preserving the underlying semantics of nominal attributes and the (potential) correlation between them. In this paper we tackle this challenge by proposing a semantically-grounded version of numerical correlated noise addition that, by relying on structured knowledge sources (ontologies), is capable of perturbing/masking multivariate nominal attributes while reasonably preserving their semantics and correlations.
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Acknowledgements
This work was supported by the EU Commission under the H2020 project “CLARUS”, by the Spanish Government through projects TIN2014-57364-C2-R “SmartGlacis”, TIN2011-27076-C03-01 “Co-Privacy” and TIN2015-70054-REDC “Red de excelencia Consolider ARES” and by the Government of Catalonia under grant 2014 SGR 537. M. Batet is supported by a Postdoctoral grant from Ministry of Economy and Competitiveness (MINECO) (FPDI-2013-16589).
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Rodriguez-Garcia, M., Sánchez, D., Batet, M. (2016). Perturbative Data Protection of Multivariate Nominal Datasets. In: Domingo-Ferrer, J., Pejić-Bach, M. (eds) Privacy in Statistical Databases. PSD 2016. Lecture Notes in Computer Science(), vol 9867. Springer, Cham. https://doi.org/10.1007/978-3-319-45381-1_8
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DOI: https://doi.org/10.1007/978-3-319-45381-1_8
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