Earlier we defined zero-knowledge proofs as being proofs of a computation of a function .
We will now go beyond this, and try to define zero-knowledge proof systems for computations that proceed inductively. That is, in pieces, and potentially over different locations involving different parties in a distributed manner.
An example of an inductive computation would be the verification of the Mina blockchain. Each block producer, when they produce a new block,
verifies that previous state of the chain was arrived at correctly
verifies that their VRF evaluation is sufficient to extend the chain
verifies a transaction proof, corresponding to the correctness of executing a bundle of Mina transactions
You can imagine the whole computation of verifying the chain, from a global view as being chopped up into per-block steps, and then each step is executed by a different block producer, relying on information which is private to that block-producer (for example, the private key needed to evaluate their VRF, which is never broadcast on the network).
But, at the end of the day, we end up with exactly one proof which summarizes this entire computation.
That is what inductive SNARKs (or in my opinion less evocatively recursive SNARKs, or proof-carrying data) allow us to do: create a single proof certifying the correctness of a big computation that occurred in steps, possibly across multiple parties, and possibly with party-local private information.
Ok, so what are inductive SNARKs? Well, first let’s describe precisely the aforementioned class of distributed computations.
zk-SNARKs as defined earlier allow you to prove for efficiently computable functions statements of the form
I know such that
Another way of looking at this is that they let you prove membership in sets of the form
These are called NP sets. In intuitive terms, an NP set is one in which membership can be efficiently checked given some “witness” or helper information (which is ).
Inductive proof systems let you prove membership in sets that are inductively defined. An inductively defined set is one where membership can be efficiently checked given some helper information, but the computation is explicitly segmented into pieces.
Let’s give the definition first and then I will link to a blog post that discusses an example.
We will give a recursive definition of a few concepts. Making this mathematically well-founded would require a little bit of work which is sort of just bureaucratic, so we will not do it. The concepts we’ll define recursively are
The data of an inductive rule for a type is
a sequence of inductive sets (note the recursive reference to )
a function . So this function is like our function for NP sets, but it also takes in the previous values from other inductive sets.
The data of an inductive set over a type is
- a sequence of inductive rules for the type ,
The subset of corresponding to (which we will for now write ) is defined inductively as follows.
For , if and only if
there is some inductive rule in the sequence with function such that
there exists and 2 for each such that
Actually there is a distinction between an inductive set , the type underlying it, and the subset of that type which belongs to that set. But it is messy to explicitly keep track of this distinction for the most part so we will equivocate between the 3 concepts.
Technical note which is fine to ignore, would be more appropriately defined by saying “There exists some efficiently computed ”.
Here the notion of membership in an inductive set is recursively referred to.
See this blog post