Private Posterior distributions from Variational approximations

Understand

Privacy preserving mechanisms such as differential privacy inject additional randomness in the form of noise in the data, beyond the sampling mechanism.

• Ignoring this additional noise can lead to inaccurate and invalid inferences.
• In this paper, we incorporate the privacy mechanism explicitly into the likelihood function by treating the original data as missing, with an end goal of estimating posterior distributions over model parameters.
• This leads to a principled way of performing valid statistical inference using private data, however, the corresponding likelihoods are intractable.