Hey! I'm David, cofounder of zkSecurity and the author of the Real-World Cryptography book. I was previously a crypto architect at O(1) Labs (working on the Mina cryptocurrency), before that I was the security lead for Diem (formerly Libra) at Novi (Facebook), and a security consultant for the Cryptography Services of NCC Group. This is my blog about cryptography and security and other related topics that I find interesting.

# A note on the elliptic curve pairing checks in zero-knowledge proofs posted March 2024

As I explained here a while back, checking polynomial identities (some left-hand side is equal to some right-hand side) when polynomials are hidden using polynomial commitment schemes, gets harder and harder with multiplications. This is why we use pairings, and this is why sometimes we "linearize" our identities. If you didn't get what I just said, great! Because this is exactly what I'll explain in this post.

## Using Schwartz-Zippel with no multiplication

First, let me say that there's typically two types of "nice" polynomial commitment schemes that people use with elliptic curves: Pedersen commitments and KZG commitments.

Pedersen commitments are basically hidden random linear combinations of the coefficients of a polynomial. That is, if your polynomial is $f(x) = \sum c_i \cdot x^i$ your commitment will look like $[\sum r_i \cdot c_i] G$ for some base point $G$ and unknown random values $r_i$. This is both good and bad: since we have access to the coefficients we can try to use them to evaluate a polynomial from its commitment, but since it's a random linear combination of them things can get ugly.

On the other hand, KZG commitments can be seen as hidden evaluations of your polynomials. For the same polynomial $f$ as above, a KZG commitment of $f$ would look like $[f(s)]G$ for some unknown random point $s$. Not knowing $s$ here is much harder than not knowing the values $r_i$ in Pedersen commitments, and this is why KZG usually requires a trusted setup whereas Pedersen doesn't.

In the rest of this post we'll use KZG commitments to prove identities.

Let's use $[a]$ to mean "commitment of the polynomial $a(x)$", then you can easily check that $a(x) = b(x)$ knowing only the commitments to $a(x)$ and $b(x)$ by checking that $[a] = [b]$ or $[a] - [b] = [0]$. This is because of the Schwartz-Zippel (S-Z) lemma which tells us that checking this identity at a random point is convincing with high-enough probability.

When multiplication with scalars is required, then things are fine. As you can do $i \cdot [a]$ to obtain $[i \cdot a]$, checking that $i \cdot a = j \cdot b$ is as simple as checking that $i \cdot [a] - j \cdot [b] = [0]$.

This post is about explaining how pairing helps us when we want to check an identity that involves multiplying $a$ and $b$ together.

## Using elliptic curve pairings for a single multiplication

It turns out that elliptic curve pairings allow us to perform a single multiplication. Meaning that once things get multiplied, they move to a different planet where things can only get added together and compared. No more multiplications.

Pairings give you this function $e$ which allows you to move things in the exponent like this: $e([a], [b]) = e([1], [1])^{ab}$. Where, remember, $ab$ is the multiplication of the two polynomials evaluated at a random point: $a(s) \cdot b(s)$.

As such, if you wanted to check something like this for example: $a \cdot b = c + 3$ with commitments only, you could check the following pairings:

$$e([a], [b]) = e([c] + 3 [1], [1])$$ By the way, the left argument and the right argument of a pairing are often in different groups for "reasons". So we usually write things like this:

$$e([a]_1, [b]_2) = e([c]_1 + 3 [1]_1, [1]_2)$$ And so it is important to have commitments in the right groups if you want to be able to construct your polynomial identity check.

## Evaluations can help with more than one multiplication

But what if you want to check something like $a \cdot b \cdot c = d + 4$? Are we doomed?

We're not! One insight that plonk brought to me (which potentially came from older papers, I don't know, I'm not an academic, leave me alone), is that you can reduce the number of multiplication with "this one simple trick". Let me explain...

A typical scenario includes you wanting to check an identity like this one:

$$a(x) \cdot b(x) \cdot c(x) = d(x)$$

and you have KZG commitments to all three polynomials $[a], [b], [c]$. (So in other words, hidden evaluations of these polynomials at the same unknown random point $s$)

You can't compute the commitment of the left-hand side because you can't perform the multiplication of the three commitments.

The trick is to evaluate (using KZG) the previous identity at a different point, let's say $\zeta$, and pre-evaluate (using KZG as well) as many polynomials as you can to $\zeta$ to reduce the number of multiplications down to 0.

Note: that is, if we want to check that $a(x) - b(x) = 0$ is true, and we want to use S-Z to do that at some point $\zeta$, then we can pre-evaluate $a$ (or $b$) and check the following identity $a(\zeta) - b(x) = 0$ at some point $\zeta$ instead.

More precisely, we'll choose to pre-evaluate $b(\zeta) = \bar{b}$ and $c(\zeta) = \bar{c}$, for example. This means that we'll have to produce a quotient polynomial $q_b$ and $q_c$ such that:

1. $b(s) - \bar{b} = (s - \zeta) \cdot q_b(s)$
2. $c(s) - \bar{c} = (s - \zeta) \cdot q_c(s)$

which means that the verifier will have to perform the following two pairings (after having been sent the evaluation $\bar{b}$ and $\bar{c}$ in the clear):

1. $e([b]_1 - \bar{b} \cdot [1]_1, [1]_2) = e([x]_1 - \zeta \cdot [1]_1, [q_b]_2)$
2. $e([c]_1 - \bar{c} \cdot [1]_1, [1]_2) = e([x]_1 - \zeta \cdot [1]_1, [q_c]_2)$

Then, they'll be able to check the first identity at $\zeta$ and use $\bar{b}$ and $\bar{c}$ in place of the commitments $[b]$ and $[c]$. The verifier check will look like the following pairing (after receiving a commitment $[q]$ from the prover): $$e( \bar{b} \cdot \bar{c} \cdot [a]_1 - [d] - 0, [1]_2) = e([x]_1 - \zeta \cdot [1]_1, [q]_2)$$ which proves using KZG that $a(\zeta)b(\zeta)c(\zeta) - d(\zeta) = 0$ (which proves that the identity checks out with high probability thanks to S-Z).

## Aggregating all the KZG evaluation proofs

In the previous explanation, we actually perform 3 KZG evaluation proofs instead of one:

• $2$ pairings that are KZG evaluation proofs that pre-evaluate different polynomials from the main check at some random point $\zeta$.
• $1$ pairing that evaluates the main identity at $\zeta$, after it was linearized to get rid of any multiplication of commitments.

Pairings can be aggregated by simply creating a random linear combinations of the pairings. That is, with some random values $r_i$ we can aggregate the checks where the left-hand side is: $$b(s) - \bar{b} + r_1 (c(s) - \bar{c}) + r_2 (\bar{b} \cdot \bar{c} \cdot a(s) - d(s) - 0])$$ and the right-hand side is: $$= (s - \zeta) \cdot q_b(s) + r_1 ((s - \zeta) \cdot q_c(s)) + r_2 ((s - \zeta) \cdot q(s))$$

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# Plonk's permutation, the definitive explanation posted March 2024

I've recorded a video on how the plonk permutation works here, but I thought I would write a more incremental explanation about it for those who want MOAR! If things don't make sense in this explanation, I'm happy to dig into specifics in more detail, just ask in the comments! Don't forget your companion eprint paper.

## Multiset equality check

Suppose that you have two ordered sets) of values $D = {d_1, d_2, d_3, d_4}$ and $E = {e_1, e_2, e_3, e_4}$, and that you want to check that they contain the same values. That is, you want to check that there exists a permutation of the elements of $D$ (or $E$) such that the multisets (sets where some values can repeat) are the same, but you don't care about which permutation exactly gets you there. You're willing to accept ANY permutation.

$${d_1, d_2, d_3, d_4} = \text{some_permutation}({e_1, e_2, e_3, e_4})$$ For example, it could be that re-ordering $E$ as ${e_2, e_3, e_1, e_4}$ gives us exactly $D$.

### Trick 1: multiply things to reduce to a single value

One way to do perform our multiset equality check is to compare the product of elements on both sides:

$$d_1 \cdot d_2 \cdot d_3 \cdot d_4 = e_1 \cdot e_2 \cdot e_3 \cdot e_4$$

If the two sets contain the same values then our identity checks out. But the reverse is not true, and thus this scheme is not secure.

Can you see why?

For example, $D = (1, 1, 1, 15)$ and $E = (3, 5, 1, 1)$ are obviously different multisets, yet the product of their elements will match!

### Trick 2: use polynomials, because maybe it will help...

What we can do to fix this issue is to encode the values of each lists as roots of two polynomials:

• $d(x) = (x - d_1)(x - d_2)(x - d_3)(x - d_4)$
• $e(x) = (x - e_1)(x - e_2)(x - e_3)(x - e_4)$

These two polynomials are equal if they have the same roots with the same multiplicities (meaning that if a root repeats, it must repeat the same number of times).

### Trick 3: optimize polynomial identities with Schwartz-Zippel

Now is time to use the Schwartz-Zippel lemma to optimize the comparison of polynomials! Our lemma tells us that if two polynomials are equal, then they are equal on all points, but if two polynomials are not equal, then they differ on MOST points.

So one easy way to check that they match with high probability is to sample a random evaluation point, let's say some random $\gamma$. Then evaluate both polynomials at that random point $\gamma$ to see if their evaluations match: $$(\gamma - d_1)(\gamma - d_2)(\gamma - d_3)(\gamma - d_4) = (\gamma - e_1)(\gamma - e_2)(\gamma - e_3)(\gamma - e_4)$$

## Permutation check

The previous check is not useful for wiring different cells within some execution trace. There is no specific "permutation" being enforced. So we can't use it as in in plonk to implement our copy constraints.

### Trick 4: random linear combinations to encode tuples

To enforce a permutation, we can compare tuples of elements instead! For example, let's say we want to enforce that $E$ must be re-ordered using the permutation $(1 3 2) (4)$ in cycle notation. Then we would try to do the following identity check: $$((1, d_1), (2, d_2), (3, d_3), (4, d_4)) = ((2, e_1), (3, e_2), (1, e_3), (4, e_4))$$ Here, we are enforcing that $d_1$ is equal to $e_3$, and that $d_2$ is equal to $e_1$, etc. This allows us to re-order the elements of $E$: $$((1, d_1), (2, d_2), (3, d_3), (4, d_4)) = ((1, e_3), (2, e_1), (3, e_2), (4, e_4))$$ But how can we encode our tuples into the polynomials we've seen previously? The trick is to use a random linear combination! (And that is often the answer in a bunch of ZK protocol.)

So if we want to encode $(2, d_2)$ in an equation, for example, we write $2 + \beta \cdot d_2$ for some random value $\beta$.

Note: The rationale behind this idea is still due to Schwartz-Zippel: if you have two tuples $(a,b)$ and $(a', b')$ you know that the polynomials $a + x \cdot b$ is the same as the polynomial $a' + x \cdot b'$ if $a = a'$ and $b = b'$, or if you have $x = \frac{a' - a}{b - b'}$ . If $x$ is chosen at random, the probability that it is exactly that value is $\frac{1}{N}$ with $N$ the size of your sampling domain (i.e. the size of your field) which is highly unlikely.

So now we can encode the previous lists of tuples as these polynomials:

• $d(x, y) = (1 + y \cdot d_1 - x)(2 + y \cdot d_2 - x)(3 + y \cdot d_3 - x)(4 + y \cdot d_4 - x)$
• $e(x, y) = (2 + y \cdot e_1 - x)(3 + y \cdot e_2 - x)(1 + y \cdot e_3 - x)(4 + y \cdot e_4 - x)$

And then reduce both polynomials to a single value by sampling random values for $x$ and $y$. Which gives us:

• $(1 + \beta \cdot d_1 - \gamma)(2 + \beta \cdot d_2 - \gamma)(3 + \beta \cdot d_3 - \gamma)(4 + \beta \cdot d_4 - \gamma)$
• $(2 + \beta \cdot e_1 - \gamma)(3 + \beta \cdot e_2 - \gamma)(1 + \beta \cdot e_3 - \gamma)(4 + \beta \cdot e_4 - \gamma)$

If these two values match, with overwhelming probability we have that the two polynomials match and thus our permutation of $E$ matches $D$.

## Wiring within a single execution trace column

Let's now see how we can use the (optimized) checks we've learn previously in plonk. We will first learn how to wire cells of a single execution trace column, and in the next section we will expand this to three columns (as vanilla Plonk uses three columns).

Take some moment to think about how can we use the previous stuff.

The answer is to see the execution trace as your list $E$, and then see if it is equal to a fixed permutation of it ($D$). Note that this permutation is decided when you write your circuit, and precomputed into the verifier key in Plonk.

Remember that the formula we're trying to check is the following for some random $\beta$ and $\gamma$, and for some permutation function $\sigma$ that we defined:

$$\prod_{i=1} (i + \beta \cdot d[i] - \gamma) = \prod_{i=1} (\sigma(i) + \beta \cdot e[i] - \gamma)$$

### Trick 5: write a circuit for the permutation check

To enforce the previous check, we will write a mini-circuit (yes an actual circuit!) which will progressively accumulate the result of dividing the left-hand side with the right-hand side. This circuit only requires one variable/register we'll call $z$ (and so it will add a new column $z$ in our execution trace) which will start with the initial value 1 and will end with the following value:

$$\prod_{i=1} \frac{i+\beta \cdot d[i] - \gamma}{\sigma(i) + \beta \cdot e[i] - \gamma} = 1$$

Let's rewrite it using only the first wire/column $a$ of Plonk, and using our generator $\omega$ as index in our tuples (because this is how we handily index things in Plonk):

$$\prod_{i=1} \frac{\omega^i+\beta \cdot a[i] - \gamma}{\sigma(\omega^i) + \beta \cdot a[i] - \gamma} = 1$$

We can then constrain the last value to be equal to 1, which will enforce that the two polynomials encoding our list of value and its permutation are equal (with overwhelming probability).

In plonk, a gate can only access variables/registers from the same row. So we will use the following extra gate (reordering the previous equation, as we can't divide in a circuit) throughout the circuit:

$$z[i+1] \cdot (\sigma(i) + \beta \cdot a[i] - \gamma) = z[i] \cdot (i + \beta \cdot a[i] - \gamma)$$ Now, how do we encode this gate in the circuit? The astute eye will have noticed that we are using a cell of the next row ($z[i+1]$) which we haven't done in Plonk so far.

### Trick 6: you're in a multiplicative subgroup, remember?

Enforcing things across rows is actually possible in plonk because we encode our polynomials in a multiplicative subgroup of our field! Due to this, we can reach for the next value(s) by multiplying an evaluation point with the subgroup's generator.

That is, values are encoded in our polynomials at evaluation points $\omega, \omega^2, \omega^3, \cdots$, and so multiplying an evaluation point by $\omega$ (the generator) brings you to the next cell in an execution trace.

As such, the verifier will later try to enforce that the following identity checks out in the multiplicative subgroup:

$$z(x \cdot \omega) \cdot (\sigma(x) + \beta \cdot a(x) - \gamma) = z(x) \cdot (x + \beta \cdot a(x) - \gamma)$$

Note: This concept was generalized in turboplonk, and is used extensively in the AIR arithmetization (used by STARKs). This is also the reason why in Plonk we have to evaluate the $z$ polynomial at $\zeta \omega$.

There will also be two additional gates: one that checks that the initial value is 1, and one that check that the last value is 1, both applied only to their respective rows. One trick that Plonk uses is that the last value is actually obtained in the last row. As last_value + 1 = 0 in our multiplicative subgroup, we have that $z[\text{last_value} + 1] = z[0]$ is constrained automatically. As such, checking that $z[0] = 1$ is enough.

You can see these two gates added to the vanilla plonk gate in the computation of the quotient polynomial $t$ in plonk. Take a look at this screenshot of the round 3 of the protocol, and squint really hard to ignore the division by $Z_H(X)$, the powers of $\alpha$ being used to aggregate the different gate checks, and the fact that $b$ and $c$ (the other wires/columns) are used:

The first line in the computation of $t$ is the vanilla plonk gate (that allows you to do multiplication and addition); the last line constrains that the first value of $z$ is $1$; and the other lines encode the permutation gate as I described (again, if you ignore the terms involving $b$ and $c$).

### Trick 7: create your execution trace in steps

There's something worthy of note: the extra execution trace column $z$ contains values that use other execution trace columns. For this reason, the other execution trace columns must be fixed BEFORE anything is done with the permutation column $z$.

In Plonk, this is done by waiting for the prover to send commitments of $a$, $b$, and $c$ to the verifier, before producing the random challenges $\beta$ and $\gamma$ that will be used by the prover to produce the values of $z$.

## Wiring multiple execution trace columns

The previous check only works within the cells of a single execution trace, how does Plonk generalizes this to several execution trace columns?

Remember: we indexed our first execution trace column with the values of our circuit domain (that multiplicative subgroup), we simply have to find a way to index the other columns with distinct values.

### Trick 8: use cosets

A coset is simply a set that is the same size as another set, but that is completely disjoint from that set. Handily, a coset is also defined as something that's very easy to compute if you know a subgroup: just multiply it with some element $k$.

Since we want a similar-but-different set from the elements of our multiplicative subgroup, we can use cosets!

Plonk produces the values $k_1$ and $k_2$ (which can be the values $2$ and $3$, for example), which when multiplied with the values of our multiplicative subgroup (${\omega, \omega^2, \omega^3, \cdots}$) produces a different set of the same size. It's not a subgroup anymore, but who cares!

We now have to create three different permutations, one for each set, and each permutation can point to the index of any of the sets.

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# Are you into finding bugs and learning ZK? Here's a challenge for you posted February 2024

Spent some time to write a challenge focused on GKR (the proof system) on top of the gnark framework (which is used to write ZK circuits in Golang).

It was a lot of fun and I hope that some people are inspired to try to break it :)

We're using the challenge to hire people who are interested in doing security work in the ZK space, so if that interests you, or if you purely want a new challenge, try it out here: https://github.com/zksecurity/zkBank

And of course, since this is an active wargame) please do not release your own solution or write up!

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My previous article on zkBitcoin blew up, and we got 100 Github stars, a number of contributions on the repository, and some interest from a number of projects, and all of that in a single day!

So as requested, I made a number of videos to explain what zkBitcoin is.

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# Zero-knowledge proofs in stateful applications posted January 2024

Something that might not be immediately obvious if you're not used to zero-knowledgifying your applications, is that the provable circuits you end up using are pure functions. They do not have access to long-lasting memory and cannot have side effects. They just take some input, and produce some output.

Note: circuits are actually not strictly pure, as they are non-deterministic. For example, you might be able to use out-of-circuit randomness in your circuit.

So when mutation of persistent state is needed, you need to provide the previous state as input, and return the new state as output. This not only produces a constraint on the previous state (time of read VS time of write issues), but it also limits the size of your state.

The problem of update conflicts comes when one designs a protocol in which multiple participants decide to update the same value, and do so using local execution. That is, instead of having a central service that executes some update logic sequentially, participants can submit the result of their updates in parallel. In this situation, each participant locally executes the logic on the current state assuming that it will not have changed. But this doesn't work as soon as someone else updates the shared value. In practice, someone's update will invalidate someone else's.

The second issue of state size is usually solved with Merkle trees, which allow you to compress your state in a verifiable way, and allow you to access or update the state without having to decompress the ENTIRE state.

That's all.

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# Verifying zero-knowledge proofs on Bitcoin? posted January 2024

A few months ago Ivan told me "how cool would it be if we could verify zero-knowledge proofs on Bitcoin?" A week later, we had a prototype of the best solution we could come up with: a multi-party computation to manage a Bitcoin wallet, and a committee willing to unlock funds only in the presence of valid zero-knowledge proofs. A few iterations later and we had something a bit cooler: stateful apps with states that can be tracked on-chain, and committee members that don't need to know anything about Bitcoin. Someone might put it this way: a Bitcoin L2 with minimal trust assumption of a "canonical" Bitcoin blockchain.

From what we understand, a better way to verify zero-knowledge proofs on Bitcoin is not going to happen, and this is the best we ca have. And we built it! And we're running it in testnet. Try it here!

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# What's out there for ECDSA threshold signatures posted January 2024

In the realm of multi-party computation (MPC) protocols, threshold signing is the protocol that address how multiple participants can sign something under a "shared" private key. In other words, instead of one guy signing something with a private key, we want $N$ guys doing the same thing and obtaining the same result without any of them actually knowing the private key (each of them holds a share of the private key, revealing nothing about the private key itself).

The threshold part means that not every participant who has a share has to participate. If there's $N$ participants, then only $t < N$ has to participate for the protocol to succeed. The $t$ and $N$ depend on the protocol you want to design, on the overhead you're willing to eat, the security you want to attain, etc.

Threshold protocols are not just for signing, they're everywhere. The NIST has a Multi-Party Threshold Cryptography competition, in which you can see proposals for threshold signing, but also threshold decryption, threshold key exchanges, and others.

This post is about threshold signatures for ECDSA specifically, as it is the most commonly used signature scheme and so has attracted a number of researchers. In addition, I'm only going to talk about the history of it, because I haven't written an actual explainer on how these works, and because the history of threshold signing for ECDSA is really messy and confusing and understanding what constructions exist out there is near impossible due to the naming collisions and the number of papers released without proper nicknames (unlike FROST, which is the leading threshold signing algorithm for schnorr signatures).

So here we are, the main line of work for ECDSA threshold signatures goes something like this, and seems to mainly involve two Gs (Gennaro and Goldfeder):

1. GG18. This paper is more officially called "Fast Multiparty Threshold ECDSA with Fast Trustless Setup" and improves on BGG: Using level-1 homomorphic encryption to improve threshold DSA signatures for bitcoin wallet security (2017) and GGN: Threshold-optimal dsa/ecdsa signatures and an application to bitcoin wallet security (2016).
2. GG19. This has the same name as GG18, but fixes some of the issues in GG18. I think this is because GG18 was published in a journal, so they couldn't update it. But GG18 on eprint is the updated GG19 one. (Yet few people refer to it as GG19.) It fixes a number of bugs, including the ones brought by the Alpha-Rays attack, and A note about the security of GG18.
3. GG20. This paper is officially called "One Round Threshold ECDSA with Identifiable Abort" and builds on top of GG18/GG19 to introduce the ability to identify who caused the abort. (In other words, who messed up if something was messed up during the multi-party computation.) Note that there are still some bugs in this paper.
4. CGGMP21. This one combines GG20 with CMP20 (another work on threshold signatures). This is supposed to be the latest work in this line of work and is probably the only version that has no known issues.

Note that there's also another line of work that happened in parallel from another team, and which is similar to GG18 except that they have different bugs: Lindell-Nof: Fast secure multiparty ecdsa with practical distributed key generation and applications to cryptocurrency custody (2018).

PS: thanks to Rosario Gennaro for help figuring this out :)

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