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 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. With our construction, prover runtime improves significantly over the baseline by a factor of 90. Verification time increases by 36%, but is less than comparable approaches that do not arithmetize hash functions or signatures. Our SNARKs are specifically suited to applications in cyber-physical control systems. These are often safety critical and need to be protected against malicious actors as well as non-malicious but unintended system failures due to design errors or random faults in components. To demonstrate relevance and practicality, we implement and benchmark our new SNARKs in a sample real-world scenario with an aircraft flight control system.