Our paper "Private Sampling with Identifiable Cheaters" got accepted at PETS 2023! In the paper, we describe an approach to verifiably sample from probability distributions. Our approach can be applied in the domain of secure multiparty computation where it should be guaranteed that values were honestly sampled from a given probability distribution, even if all but one party are compromised and try to cheat. Technically, we realize our approach by the construction of tailored "zero knowledge proofs".
