Paper accepted at the Journal of Privacy and Confidentiality, 2020
Ming, Thierry, and Sirine had a paper accepted at the Journal of Privacy and Confidentiality, 2020. This paper proposes a generic mechanism to efficiently release differentially private synthetic versions of high-dimensional datasets with high utility. The core technique in our mechanism is the use of copulas, which are functions representing dependencies among random variables with a multivariate distribution. Specifically, we use the Gaussian copula to define dependencies of attributes in the input dataset, whose rows are modelled as samples from an unknown multivariate distribution, and then sample synthetic records through this copula.
- Hassan Jameel Asghar, Ming Ding, Thierry Rakotoarivelo, Sirine Mrabet, Dali Kaafar, “Differentially Private Release of High-Dimensional Datasets using the Gaussian Copula,” the Journal of Privacy and Confidentiality (JPC), 2020. [https://doi.org/10.29012/jpc.686]
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