Deim Seminar
Title
Data shuffling for statistical database privacy
Conferenciant
Prof. Krish Muralidhar
Professor/a organitzador/a
Josep Domingo Ferrer
Institution
University of Kentucky, USA
Date
23-11-2011 12:00
Summary
This study discusses a new procedure for masking confidential numerical data-a procedure called data shuffling -in which the values of the confidential variables are "shuffled" among observations. The shuffled data provides a high level of data utility and minimizes the risk of disclosure. From a practical perspective, data shuffling overcomes reservations about using perturbed or modified confidential data because it retains all the desirable properties of perturbation methods and performs better than other masking techniques in both data utility and disclosure risk. In addition, data shuffling can be implemented using only rank-order data, and thus provides a nonparametric method for masking. We illustrate the applicability of data shuffling.
Place
Aula 209
Language
Anglès
