  Analyses that fulfill differential privacy provide plausible deniability to
individuals while allowing analysts to extract insights from data. However,
beyond an often acceptable accuracy tradeoff, these statistical disclosure
techniques generally inhibit the verifiability of the provided information, as
one cannot check the correctness of the participants' truthful information, the
differentially private mechanism, or the unbiased random number generation.
While related work has already discussed this opportunity, an efficient
implementation with a precise bound on errors and corresponding proofs of the
differential privacy property is so far missing. In this paper, we follow an
approach based on zero-knowledge proofs~(ZKPs), in specific succinct
non-interactive arguments of knowledge, as a verifiable computation technique
to prove the correctness of a differentially private query output. In
particular, we ensure the guarantees of differential privacy hold despite the
limitations of ZKPs that operate on finite fields and have limited branching
capabilities. We demonstrate that our approach has practical performance and
discuss how practitioners could employ our primitives to verifiably query
individuals' age from their digitally signed ID card in a differentially
private manner.
