Differential Privacy and Survey Sampling
By: Daniel Bernard Bonnéry, Julien Jamme
Potential Business Impact:
Protects private data when counting people.
The Horvitz-Thompson estimate of a total can be seen as as differentially private mechanism applied to this population total. We provide forumlae to compute the $\epsilon$ and $\delta$ parameter for this specific mecanism, coupled or not coupled with the addition of a Laplace or a Gaussian noise. This allows to determine the scale of the Laplace privacy mechanism to be added to reach a specified level of privacy, expressed in terms of $\epsilon,\delta$ differential privacy. In particular, we provide simple formulae for the special case of simple random sampling on binary data.
Similar Papers
Improving Statistical Privacy by Subsampling
Cryptography and Security
Protects secrets by adding random noise to data.
Differentially Private Community Detection in $h$-uniform Hypergraphs
Information Theory
Keeps private data safe when sharing network maps.
Near-optimal algorithms for private estimation and sequential testing of collision probability
Machine Learning (Stat)
Measures how spread out data is, privately.