Paper 2025/404

SNARKs for Stateful Computations on Authenticated Data

Abstract

We present a new generalization of (zk-)SNARKs combining two additional features at the same time. Besides the verification of correct computation, our new SNARKs also allow, first, the verification of input data authenticity. Specifically, a verifier can confirm that the input to the computation originated from a trusted source. Second, our SNARKs support verification of stateful computations across multiple rounds, ensuring that the output of the current round correctly depends on the internal state of the previous round. Our SNARKs are specifically suited to applications in cyber-physical control systems, where computations are periodically carried out and need to be checked immediately. Our focus is on concrete practicality, so we abstain from arithmetizing hash functions or signatures in our SNARKs. Rather, we modify the internals of an existing SNARK to extend its functionality. Additionally, we present new optimizations to reduce proof size, prover time, and verification time in our setting. With our construction, prover runtime improves significantly over the baseline by a factor of 89. Verification time is 70 % less for computations on authenticated data and 33 % less for stateful computations. To demonstrate relevance and practicality, we implement and benchmark our new SNARKs in a sample real-world scenario with a (simple) quadcopter flight control system.