Supplemental Materials Data Augmentation for Bayesian Inference from Privatized Data S 1 Statement on Societal Impacts

We do not foresee direct negative societal impact from the current work. Also, one may argue that our work is catalytic to enhancing the'disclosure risk' of individuals, i.e. an adversary might be able to make accurate Granted, no existing privacy frameworks can guard against this. We prove its ergodicity in Theorem S-3.1, which implies Theorem 3.3 . The model is such that the set { x: f ( x |) > 0 } does not depend on . The Metropolis-within-Gibbs sampler is aperiodic by construction, since some proposals can be rejected.