In this post we aim to give an overview of zk-SNARKs from a practical viewpoint. We will treat the actual math as a black box and try to develop some intuitions around how we can use them. We'll also give a simple application of the recent work on integrating zk-SNARKs in Ethereum.

Zero-Knowledge Proofs

The goal of zero-knowledge proofs is for a verifier to be able to convince herself that a prover possesses knowledge of a secret parameter, called a witness, satisfying some relation, without revealing the witness to the verifier or anyone else.

We can think of this more concretely as having a program, denoted C, taking two inputs: C(x, w). The input x is the public input, and w is the secret witness input. The output of the program is boolean, i.e. either true or false. The goal then is given a specific public input x, prove that the prover knows a secret input w such that C(x,w) == true.

We are specifically going to discuss non-interactive zero knowledge proofs. This means that the proof itself is a blob of data that can be verified without any interaction from the prover.

Example Program

Suppose Bob is given a hash H of some value, and he wishes to have a proof that Alice knows the value s that hashes to H. Normally Alice would prove this by giving s to Bob, after which Bob would compute the hash and check that it equals H.

However, suppose Alice doesn’t want to reveal the value s to Bob but instead she just wants to prove that she knows the value. She can use a zk-SNARK for this.

We can describe Alice’s scenario using the following program, here written as a Javascript function:

function C(x, w) { return ( sha256(w) == x );}

In other words: the program takes in a public hash x and a secret value w and returns true if the SHA–256 hash of w equals x.

Translating Alice’s problem using the function C(x,w) we see that Alice needs to create a proof that she possesses s such that C(H, s) == true, without having to reveal s. This is the general problem that zk-SNARKs solve.

Definition of a zk-SNARK

A zk-SNARK consists of three algorithms G, P, V defined as follows:

The key generator G takes a secret parameter lambda and a program C, and generates two publicly available keys, a proving key pk, and a verification key vk. These keys are public parameters that only need to be generated once for a given program C.

The prover P takes as input the proving key pk, a public input x and a private witness w. The algorithm generates a proof prf = P(pk, x, w) that the prover knows a witness w and that the witness satisfies the program.

The verifier V computes V(vk, x, prf) which returns true if the proof is correct, and false otherwise. Thus this function returns true if the prover knows a witness w satisfying C(x,w) == true.

Note here the secret parameter lambda used in the generator. This parameter sometimes makes it tricky to use zk-SNARKs in real-world applications. The reason for this is that anyone who knows this parameter can generate fake proofs. Specifically, given any program C and public input x a person who knows lambda can generate a proof fake_prf such that V(vk, x, fake_prf) evaluates to true without knowledge of the secret w.

Thus actually running the generator requires a very secure process to make sure no-one learns about and saves the parameter anywhere. This was the reason for the extremely elaborate ceremony the Zcash team conducted in order to generate the proving key and verification key, while making sure the “toxic waste” parameter lambda was destroyed in the process.

A zk-SNARK For Our Example Program

How would Alice and Bob use a zk-SNARK in practice in order for Alice to prove that she knows the secret value in the example above?

First of all, as discussed above we will use a program defined by the following function:

function C(x, w) {
return ( sha256(w) == x );

The first step is for Bob to run the generator G in order to create the proving key pk and verification key vk. First, randomly generate lambda and use that as input:

(pk, vk) = G(C, lambda)

Handle the parameter lambda with care, because if Alice learns the value of lambda she will be able to create fake proofs. Bob will share pk and vk with Alice.

Alice will now play the role of the prover. She needs to prove that she knows the value s that hashes to the known hash H. She runs the proving algorithm P using the inputs pk, H and s to generate the proof prf:

prf = P(pk, H, s)

Next Alice presents the proof prf to Bob who runs the verification function V(vk, H, prf) which would return true in this case since Alice properly knew the secret s. Bob can be confident that Alice knew the secret, but Alice did not need to reveal the secret to Bob.

Reusable Proving and Verification Keys

In our example above, the zk-SNARK cannot be used if Bob wants to prove to Alice that he knows a secret, because Alice cannot know that Bob didn’t save the lambda parameter. Bob could plausibly be able to fake proofs.

If a program is useful to many people (like the example of Zcash), a trusted independent group separate from Alice and Bob could run the generator and create the proving key pk and verification key vk in such a way that no one learns about lambda.

Anyone who trusts that the group did not cheat can then use these keys for future interactions.

zk-SNARKs in Ethereum

Developers have already started integrating zk-SNARKs into Ethereum. What does this look like? Concretely, you can add the building blocks of the verification algorithm to Ethereum in the form of precompiled contracts. Here's how: run the generator off-chain to produce the proving key and verification key. Any prover can then use the proving key to create a proof, also off-chain. You can then run the general verification algorithm inside a smart contract, using the proof, the verification key, and the public input as input parameters. You can then use the outcome of the verification algorithm to trigger other on-chain activity.

Example: Confidential Transactions

Here is a simple example of how zk-SNARKs can help with privacy on Ethereum. Suppose we have a simple token contract. Normally a token contract would have at its core a mapping from addresses to balances:

mapping (address = uint256) balances;

We are going to retain the same basic core, except replace a balance with the hash of a balance:

mapping (address = bytes32) balanceHashes;

We are not going to hide the sender or receiver of transactions. But we’ll hide the balances and sent amounts. This property is sometimes referred to as confidential transactions.

We'll use two zk-SNARKs to send tokens from one account to another. One proof is created by the sender and one by the receiver.

Normally in a token contract for a transaction of size value to be valid we need to verify the following:

balances[fromAddress] = value

Our zk-SNARKs need to prove that this holds, as well as that the updated hashes matches the updated balances.

The main idea is that the sender will use their starting balance and the transaction value as private inputs. As public inputs, they use hashes of starting balance, ending balance, and value. Similarly, the receiver will use starting balance and value as secret inputs. As public inputs, they use hashes of starting balance, ending balance, and value.

Below is the program we will use for the sender zk-SNARK, where as before x represents the public input, and w represents the private input.

function senderFunction(x, w) {
return (
 w.value &&
sha256(w.value) == x.hashValue &&
sha256(w.senderBalanceBefore) == x.hashSenderBalanceBefore &&
sha256(w.senderBalanceBefore - w.value) == x.hashSenderBalanceAfter

The program used by the receiver is below:

function receiverFunction(x, w) {
return (
sha256(w.value) == x.hashValue &&
sha256(w.receiverBalanceBefore) == x.hashReceiverBalanceBefore &&
sha256(w.receiverBalanceBefore + w.value) == x.hashReceiverBalanceAfter

The programs check that the sending balance is larger than the value being sent, as well as checking that all hashes match. A trusted set of people would generate the proving and verification keys for our zk-SNARKs. Let’s call them confTxSenderPk, confTxSenderVk, confTxReceiverPk and confTxReceiverVk.

Using the zk-SNARKs in a token contract would look something like this:

function transfer(address _to, bytes32 hashValue, bytes32 hashSenderBalanceAfter, bytes32 hashReceiverBalanceAfter, bytes zkProofSender, bytes zkProofReceiver) {
bytes32 hashSenderBalanceBefore = balanceHashes[msg.sender];
bytes32 hashReceiverBalanceBefore = balanceHashes[_to];

bool senderProofIsCorrect = zksnarkverify(confTxSenderVk, [hashSenderBalanceBefore, hashSenderBalanceAfter, hashValue], zkProofSender);
bool receiverProofIsCorrect = zksnarkverify(confTxReceiverVk, [hashReceiverBalanceBefore, hashReceiverBalanceAfter, hashValue], zkProofReceiver);
if(senderProofIsCorrect && receiverProofIsCorrect) {
balanceHashes[msg.sender] = hashSenderBalanceAfter;
balanceHashes[_to] = hashReceiverBalanceAfter;

Thus the only updates on the blockchain are the hashes of the balances and not the balances themselves. However, we can know that all the balances are correctly updated because we can check ourselves that the proof has been verified.


The above confidential transaction scheme is mainly to give a practical example of how one can use zk-SNARKs on Ethereum. To create a robust confidential transaction scheme, we would need to address a number of issues:

  • Users would need to keep track of their balances client-side, and if you lose the balance those tokens are unrecoverable. The balances could perhaps be stored encrypted on-chain with a key derived from the signing key.

  • Balances need to use 32 bytes of data and encode entropy to prevent the ability to reverse hashes to figure out balances.

  • Need to deal with the edge case of sending to an unused address.

  • The sender needs to interact with the receiver in order to send. One could potentially have a system where the sender uses their proof to initiate the transaction. The receiver can then see on the blockchain that they have a “pending incoming transaction” and can finalize it.