Presented by:
Jordi Soria-Comas
Date:
Friday 2nd December 2016 - 14:00 to 15:00
Venue:
INI Seminar Room 2
Abstract:
We
explore two different aspects of differential privacy. First we explore the optimality of noise distributions in noise addition. In particular, we
show that the Laplace distribution is nearly optimal in the univariate case, but not in
the multivariate case. Optimal distributions are described. Then we explore the generation
of differentially private data sets via perturbative masking of the original
records. This approach is
remarkably more efficient than histogram-based approaches but a naive application of it may completely damage the data utility. In particular, we analyze the use of microaggregation to reduce the
sensitivity and, thus, the amount of noise required to attain differential privacy.
remarkably more efficient than histogram-based approaches but a naive application of it may completely damage the data utility. In particular, we analyze the use of microaggregation to reduce the
sensitivity and, thus, the amount of noise required to attain differential privacy.
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