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Topics in differential privacy: optimal noise and record perturbation baseddata sets

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DLA - Data Linkage and Anonymisation

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.

This talk is part of the Isaac Newton Institute Seminar Series series.

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