Finally we can discuss zk-SNARKs. In these notes right now I will not start with intuitive motivations for SNARKs because I can’t think of them right now, but maybe later.

Let be a program (in some yet to be specified programming language). For , zk-SNARKs let you produce proofs for statements of the form

I know such that

in such a way that no information about is revealed by the proof (this is the zero-knowledge or ZK part of them).

In other words, zk-SNARKs allow you to prove execution of programs, where you are also allowed to hide a selected portion (i.e., the portion) of the data involved in the computation.

We could also phrase zk-SNARKs in terms of computing functions , which seems more general, but actually this would be taken care of by the previous sense where we can only use boolean-valued functions, by proving against the function defined by .

SNARK stands for Succinct Non-interactive ARguments of Knowledge. If it also satisfies zero-knowledge, then it is called a zk-SNARK. Preprocessing SNARKs allow the verifier to precompute some encodings of the relation to run independently of the instance length. Definitions of these schemes owe the succinctness property to the fact that both the verifier and the proof length are (at most) polylogarithmic in the size of the computation (the witness length). Recent schemes such as Plonk, go beyond that definition and provide zkSNARKs with constant proof length. Unlike the verifier in preprocessing SNARKs, the prover can’t be faster than linear, as it has to read the circuit at least.

Expressing computations with “iterating over trace tables”

We will define a notion of computation (a programming language basically) for which it is easy to construct zk-SNARKs.