halo2
IMPORTANT: This library is in beta, and should not be used in production software.
Documentation
Minimum Supported Rust Version
Requires Rust 1.51 or higher.
Minimum supported Rust version can be changed in the future, but it will be done with a minor version bump.
Controlling parallelism
halo2
currently uses rayon for parallel computation.
The RAYON_NUM_THREADS
environment variable can be used to set the number of threads.
License
Copyright 20202021 The Electric Coin Company.
You may use this package under the Bootstrap Open Source Licence, version 1.0,
or at your option, any later version. See the file COPYING
for
more details, and LICENSEBOSL
for the terms of the Bootstrap
Open Source Licence, version 1.0.
The purpose of the BOSL is to allow commercial improvements to the package while ensuring that all improvements are open source. See here for why the BOSL exists.
Concepts
First we'll describe the concepts behind zeroknowledge proof systems; the arithmetization (kind of circuit description) used by Halo 2; and the abstractions we use to build circuit implementations.
Proof systems
The aim of any proof system is to be able to prove interesting mathematical or cryptographic statements.
Typically, in a given protocol we will want to prove families of statements that differ in their public inputs. The prover will also need to show that they know some private inputs that make the statement hold.
To do this we write down a relation, $R$, that specifies which combinations of public and private inputs are valid.
The terminology above is intended to be aligned with the ZKProof Community Reference.
To be precise, we should distinguish between the relation $R$, and its implementation to be used in a proof system. We call the latter a circuit.
The language that we use to express circuits for a particular proof system is called an arithmetization. Usually, an arithmetization will define circuits in terms of polynomial constraints on variables over a field.
The process of expressing a particular relation as a circuit is also sometimes called "arithmetization", but we'll avoid that usage.
To create a proof of a statement, the prover will need to know the private inputs, and also intermediate values, called advice values, that are used by the circuit.
We assume that we can compute advice values efficiently from the private and public inputs. The particular advice values will depend on how we write the circuit, not only on the highlevel statement.
The private inputs and advice values are collectively called a witness.
Some authors use "witness" as just a synonym for private inputs. But in our usage, a witness includes advice, i.e. it includes all values that the prover supplies to the circuit.
For example, suppose that we want to prove knowledge of a preimage $x$ of a hash function $H$ for a digest $y$:

The private input would be the preimage $x$.

The public input would be the digest $y$.

The relation would be ${(x,y):H(x)=y}$.

For a particular public input $Y$, the statement would be: ${(x):H(x)=Y}$.

The advice would be all of the intermediate values in the circuit implementing the hash function. The witness would be $x$ and the advice.
A Noninteractive Argument allows a prover to create a proof for a given statement and witness. The proof is data that can be used to convince a verifier that there exists a witness for which the statement holds. The security property that such proofs cannot falsely convince a verifier is called soundness.
A Noninteractive Argument of Knowledge (NARK) further convinces the verifier that the prover knew a witness for which the statement holds. This security property is called knowledge soundness, and it implies soundness.
In practice knowledge soundness is more useful for cryptographic protocols than soundness: if we are interested in whether Alice holds a secret key in some protocol, say, we need Alice to prove that she knows the key, not just that it exists.
Knowledge soundness is formalized by saying that an extractor, which can observe precisely how the proof is generated, must be able to compute the witness.
This property is subtle given that proofs can be malleable. That is, depending on the proof system it may be possible to take an existing proof (or set of proofs) and, without knowing the witness(es), modify it/them to produce a distinct proof of the same or a related statement. Higherlevel protocols that use malleable proof systems need to take this into account.
Even without malleability, proofs can also potentially be replayed. For instance, we would not want Alice in our example to be able to present a proof generated by someone else, and have that be taken as a demonstration that she knew the key.
If a proof yields no information about the witness (other than that a witness exists and was known to the prover), then we say that the proof system is zero knowledge.
If a proof system produces short proofs —i.e. of length polylogarithmic in the circuit size— then we say that it is succinct. A succinct NARK is called a SNARK (Succinct NonInteractive Argument of Knowledge).
By this definition, a SNARK need not have verification time polylogarithmic in the circuit size. Some papers use the term efficient to describe a SNARK with that property, but we'll avoid that term since it's ambiguous for SNARKs that support amortized or recursive verification, which we'll get to later.
A zkSNARK is a zeroknowledge SNARK.
PLONKish Arithmetization
The arithmetization used by Halo 2 comes from PLONK, or more precisely its extension UltraPLONK that supports custom gates and lookup arguments. We'll call it PLONKish.
PLONKish circuits are defined in terms of a rectangular matrix of values. We refer to rows, columns, and cells of this matrix with the conventional meanings.
A PLONKish circuit depends on a configuration:

A finite field $F$, where cell values (for a given statement and witness) will be elements of $F$.

The number of columns in the matrix, and a specification of each column as being fixed, advice, or instance. Fixed columns are fixed by the circuit; advice columns correspond to witness values; and instance columns are normally used for public inputs (technically, they can be used for any elements shared between the prover and verifier).

A subset of the columns that can participate in equality constraints.

A polynomial degree bound.

A sequence of polynomial constraints. These are multivariate polynomials over $F$ that must evaluate to zero for each row. The variables in a polynomial constraint may refer to a cell in a given column of the current row, or a given column of another row relative to this one (with wraparound, i.e. taken modulo $n$). The maximum degree of each polynomial is given by the polynomial degree bound.

A sequence of lookup arguments defined over tuples of input expressions (which are multivariate polynomials as above) and table columns.
A PLONKish circuit also defines:

The number of rows $n$ in the matrix. $n$ must correspond to the size of a multiplicative subgroup of $F_{×}$; typically a power of two.

A sequence of equality constraints, which specify that two given cells must have equal values.

The values of the fixed columns at each row.
From a circuit description we can generate a proving key and a verification key, which are needed for the operations of proving and verification for that circuit.
Note that we specify the ordering of columns, polynomial constraints, lookup arguments, and equality constraints, even though these do not affect the meaning of the circuit. This makes it easier to define the generation of proving and verification keys as a deterministic process.
Typically, a configuration will define polynomial constraints that are switched off and on by selectors defined in fixed columns. For example, a constraint $q_{i}⋅p(...)=0$ can be switched off for a particular row $i$ by setting $q_{i}=0$. In this case we sometimes refer to a set of constraints controlled by a set of selector columns that are designed to be used together, as a gate. Typically there will be a standard gate that supports generic operations like field multiplication and division, and possibly also custom gates that support more specialized operations.
Chips
The previous section gives a fairly lowlevel description of a circuit. When implementing circuits we will typically use a higherlevel API which aims for the desirable characteristics of auditability, efficiency, modularity, and expressiveness.
Some of the terminology and concepts used in this API are taken from an analogy with integrated circuit design and layout. As for integrated circuits, the above desirable characteristics are easier to obtain by composing chips that provide efficient prebuilt implementations of particular functionality.
For example, we might have chips that implement particular cryptographic primitives such as a hash function or cipher, or algorithms like scalar multiplication or pairings.
In PLONKish circuits, it is possible to build up arbitrary logic just from standard gates that do field multiplication and addition. However, very significant efficiency gains can be obtained by using custom gates.
Using our API, we define chips that "know" how to use particular sets of custom gates. This creates an abstraction layer that isolates the implementation of a highlevel circuit from the complexity of using custom gates directly.
Even if we sometimes need to "wear two hats", by implementing both a highlevel circuit and the chips that it uses, the intention is that this separation will result in code that is easier to understand, audit, and maintain/reuse. This is partly because some potential implementation errors are ruled out by construction.
Gates in PLONKish circuits refer to cells by relative references, i.e. to the cell in a given column, and the row at a given offset relative to the one in which the gate's selector is set. We call this an offset reference when the offset is nonzero (i.e. offset references are a subset of relative references).
Relative references contrast with absolute references used in equality constraints, which can point to any cell.
The motivation for offset references is to reduce the number of columns needed in the configuration, which reduces proof size. If we did not have offset references then we would need a column to hold each value referred to by a custom gate, and we would need to use equality constraints to copy values from other cells of the circuit into that column. With offset references, we not only need fewer columns; we also do not need equality constraints to be supported for all of those columns, which improves efficiency.
In R1CS (another arithmetization which may be more familiar to some readers, but don't worry if it isn't), a circuit consists of a "sea of gates" with no semantically significant ordering. Because of offset references, the order of rows in a PLONKish circuit, on the other hand, is significant. We're going to make some simplifying assumptions and define some abstractions to tame the resulting complexity: the aim will be that, at the gadget level where we do most of our circuit construction, we will not have to deal with relative references or with gate layout explicitly.
We will partition a circuit into regions, where each region contains a disjoint subset of cells, and relative references only ever point within a region. Part of the responsibility of a chip implementation is to ensure that gates that make offset references are laid out in the correct positions in a region.
Given the set of regions and their shapes, we will use a separate floor planner to decide where (i.e. at what starting row) each region is placed. There is a default floor planner that implements a very general algorithm, but you can write your own floor planner if you need to.
Floor planning will in general leave gaps in the matrix, because the gates in a given row did not use all available columns. These are filled in —as far as possible— by gates that do not require offset references, which allows them to be placed on any row.
Chips can also define lookup tables. If more than one table is defined for the same lookup argument, we can use a tag column to specify which table is used on each row. It is also possible to perform a lookup in the union of several tables (limited by the polynomial degree bound).
Composing chips
In order to combine functionality from several chips, we compose them in a tree. The toplevel chip defines a set of fixed, advice, and instance columns, and then specifies how they should be distributed between lowerlevel chips.
In the simplest case, each lowerlevel chips will use columns disjoint from the other chips. However, it is allowed to share a column between chips. It is important to optimize the number of advice columns in particular, because that affects proof size.
The result (possibly after optimization) is a PLONKish configuration. Our circuit implementation will be parameterized on a chip, and can use any features of the supported lowerlevel chips via the toplevel chip.
Our hope is that less expert users will normally be able to find an existing chip that supports the operations they need, or only have to make minor modifications to an existing chip. Expert users will have full control to do the kind of circuit optimizations that ECC is famous for 🙂.
Gadgets
When implementing a circuit, we could use the features of the chips we've selected directly. Typically, though, we will use them via gadgets. This indirection is useful because, for reasons of efficiency and limitations imposed by PLONKish circuits, the chip interfaces will often be dependent on lowlevel implementation details. The gadget interface can provide a more convenient and stable API that abstracts away from extraneous detail.
For example, consider a hash function such as SHA256. The interface of a chip supporting
SHA256 might be dependent on internals of the hash function design such as the separation
between message schedule and compression function. The corresponding gadget interface can
provide a more convenient and familiar update
/finalize
API, and can also handle parts
of the hash function that do not need chip support, such as padding. This is similar to how
accelerated
instructions
for cryptographic primitives on CPUs are typically accessed via software libraries, rather
than directly.
Gadgets can also provide modular and reusable abstractions for circuit programming at a higher level, similar to their use in libraries such as libsnark and bellman. As well as abstracting functions, they can also abstract types, such as elliptic curve points or integers of specific sizes.
User Documentation
You're probably here because you want to write circuits? Excellent!
This section will guide you through the process of creating circuits with halo2.
Developer tools
The halo2
crate includes several utilities to help you design and implement your
circuits.
Mock prover
halo2::dev::MockProver
is a tool for debugging circuits, as well as cheaply verifying
their correctness in unit tests. The private and public inputs to the circuit are
constructed as would normally be done to create a proof, but MockProver::run
instead
creates an object that will test every constraint in the circuit directly. It returns
granular error messages that indicate which specific constraint (if any) is not satisfied.
Circuit visualizations
The devgraph
feature flag exposes several helper methods for creating graphical
representations of circuits.
On Debian systems, you will need the following additional packages:
sudo apt install cmake libexpat1dev libfreetype6dev
Circuit layout
halo2::dev::CircuitLayout
renders the circuit layout as a grid:
fn main() {
// Prepare the circuit you want to render.
// You don't need to include any witness variables.
let a = Fp::rand();
let instance = Fp::one() + Fp::one();
let lookup_table = vec![instance, a, a, Fp::zero()];
let circuit: MyCircuit<Fp> = MyCircuit {
a: None,
lookup_table,
};
// Create the area you want to draw on.
// Use SVGBackend if you want to render to .svg instead.
use plotters::prelude::*;
let root = BitMapBackend::new("layout.png", (1024, 768)).into_drawing_area();
root.fill(&WHITE).unwrap();
let root = root
.titled("Example Circuit Layout", ("sansserif", 60))
.unwrap();
halo2::dev::CircuitLayout::default()
// You can optionally render only a section of the circuit.
.view_width(0..2)
.view_height(0..16)
// You can hide labels, which can be useful with smaller areas.
.show_labels(false)
// Render the circuit onto your area!
// The first argument is the size parameter for the circuit.
.render(5, &circuit, &root)
.unwrap();
}
 Columns are layed out from left to right as instance, advice, and fixed. The order of
columns is otherwise without meaning.
 Instance columns have a white background.
 Advice columns have a red background.
 Fixed columns have a blue background.
 Regions are shown as labelled green boxes (overlaying the background colour). A region may appear as multiple boxes if some of its columns happen to not be adjacent.
 Cells that have been assigned to by the circuit will be shaded in grey. If any cells are assigned to more than once (which is usually a mistake), they will be shaded darker than the surrounding cells.
Circuit structure
halo2::dev::circuit_dot_graph
builds a DOT graph string representing the given
circuit, which can then be rendered witha variety of layout programs. The graph is built
from calls to Layouter::namespace
both within the circuit, and inside the gadgets and
chips that it uses.
fn main() {
// Prepare the circuit you want to render.
// You don't need to include any witness variables.
let a = Fp::rand();
let instance = Fp::one() + Fp::one();
let lookup_table = vec![instance, a, a, Fp::zero()];
let circuit: MyCircuit<Fp> = MyCircuit {
a: None,
lookup_table,
};
// Generate the DOT graph string.
let dot_string = halo2::dev::circuit_dot_graph(&circuit);
// Now you can either handle it in Rust, or just
// print it out to use with commandline tools.
print!("{}", dot_string);
}
Cost estimator
The costmodel
binary takes highlevel parameters for a circuit design, and estimates
the verification cost, as well as resulting proof size.
Usage: cargo run example costmodel  [OPTIONS] k
Positional arguments:
k 2^K bound on the number of rows.
Optional arguments:
h, help Print this message.
a, advice R[,R..] An advice column with the given rotations. May be repeated.
i, instance R[,R..] An instance column with the given rotations. May be repeated.
f, fixed R[,R..] A fixed column with the given rotations. May be repeated.
g, gatedegree D Maximum degree of the custom gates.
l, lookup N,I,T A lookup over N columns with max input degree I and max table degree T. May be repeated.
p, permutation N A permutation over N columns. May be repeated.
For example, to estimate the cost of a circuit with three advice columns and one fixed column (with various rotations), and a maximum gate degree of 4:
> cargo run example costmodel  a 0,1 a 0 a0,1,1 f 0 g 4 11
Finished dev [unoptimized + debuginfo] target(s) in 0.03s
Running `target/debug/examples/costmodel a 0,1 a 0 a 0,1,1 f 0 g 4 11`
Circuit {
k: 11,
max_deg: 4,
advice_columns: 3,
lookups: 0,
permutations: [],
column_queries: 7,
point_sets: 3,
estimator: Estimator,
}
Proof size: 1440 bytes
Verification: at least 81.689ms
A simple example
Let's start with a simple circuit, to introduce you to the common APIs and how they are used. The circuit will take a public input $c$, and will prove knowledge of two private inputs $a$ and $b$ such that
$a_{2}⋅b_{2}=c.$
Define instructions
Firstly, we need to define the instructions that our circuit will rely on. Instructions are the boundary between highlevel gadgets and the lowlevel circuit operations. Instructions may be as coarse or as granular as desired, but in practice you want to strike a balance between an instruction being large enough to effectively optimize its implementation, and small enough that it is meaningfully reusable.
For our circuit, we will use three instructions:
 Load a private number into the circuit.
 Multiply two numbers.
 Expose a number as a public input to the circuit.
We also need a type for a variable representing a number. Instruction interfaces provide associated types for their inputs and outputs, to allow the implementations to represent these in a way that makes the most sense for their optimization goals.
trait NumericInstructions<F: FieldExt>: Chip<F> {
/// Variable representing a number.
type Num;
/// Loads a number into the circuit as a private input.
fn load_private(&self, layouter: impl Layouter<F>, a: Option<F>) > Result<Self::Num, Error>;
/// Loads a number into the circuit as a fixed constant.
fn load_constant(&self, layouter: impl Layouter<F>, constant: F) > Result<Self::Num, Error>;
/// Returns `c = a * b`.
fn mul(
&self,
layouter: impl Layouter<F>,
a: Self::Num,
b: Self::Num,
) > Result<Self::Num, Error>;
/// Exposes a number as a public input to the circuit.
fn expose_public(
&self,
layouter: impl Layouter<F>,
num: Self::Num,
row: usize,
) > Result<(), Error>;
}
Define a chip implementation
For our circuit, we will build a chip that provides the above numeric instructions for a finite field.
/// The chip that will implement our instructions! Chips store their own
/// config, as well as type markers if necessary.
struct FieldChip<F: FieldExt> {
config: FieldConfig,
_marker: PhantomData<F>,
}
Every chip needs to implement the Chip
trait. This defines the properties of the chip
that a Layouter
may rely on when synthesizing a circuit, as well as enabling any initial
state that the chip requires to be loaded into the circuit.
impl<F: FieldExt> Chip<F> for FieldChip<F> {
type Config = FieldConfig;
type Loaded = ();
fn config(&self) > &Self::Config {
&self.config
}
fn loaded(&self) > &Self::Loaded {
&()
}
}
Configure the chip
The chip needs to be configured with the columns, permutations, and gates that will be required to implement all of the desired instructions.
/// Chip state is stored in a config struct. This is generated by the chip
/// during configuration, and then stored inside the chip.
#[derive(Clone, Debug)]
struct FieldConfig {
/// For this chip, we will use two advice columns to implement our instructions.
/// These are also the columns through which we communicate with other parts of
/// the circuit.
advice: [Column<Advice>; 2],
/// This is the public input (instance) column.
instance: Column<Instance>,
// We need a selector to enable the multiplication gate, so that we aren't placing
// any constraints on cells where `NumericInstructions::mul` is not being used.
// This is important when building larger circuits, where columns are used by
// multiple sets of instructions.
s_mul: Selector,
/// The fixed column used to load constants.
constant: Column<Fixed>,
}
impl<F: FieldExt> FieldChip<F> {
fn construct(config: <Self as Chip<F>>::Config) > Self {
Self {
config,
_marker: PhantomData,
}
}
fn configure(
meta: &mut ConstraintSystem<F>,
advice: [Column<Advice>; 2],
instance: Column<Instance>,
constant: Column<Fixed>,
) > <Self as Chip<F>>::Config {
meta.enable_equality(instance.into());
meta.enable_constant(constant);
for column in &advice {
meta.enable_equality((*column).into());
}
let s_mul = meta.selector();
// Define our multiplication gate!
meta.create_gate("mul", meta {
// To implement multiplication, we need three advice cells and a selector
// cell. We arrange them like so:
//
//  a0  a1  s_mul 
// 
//  lhs  rhs  s_mul 
//  out   
//
// Gates may refer to any relative offsets we want, but each distinct
// offset adds a cost to the proof. The most common offsets are 0 (the
// current row), 1 (the next row), and 1 (the previous row), for which
// `Rotation` has specific constructors.
let lhs = meta.query_advice(advice[0], Rotation::cur());
let rhs = meta.query_advice(advice[1], Rotation::cur());
let out = meta.query_advice(advice[0], Rotation::next());
let s_mul = meta.query_selector(s_mul);
// Finally, we return the polynomial expressions that constrain this gate.
// For our multiplication gate, we only need a single polynomial constraint.
//
// The polynomial expressions returned from `create_gate` will be
// constrained by the proving system to equal zero. Our expression
// has the following properties:
//  When s_mul = 0, any value is allowed in lhs, rhs, and out.
//  When s_mul != 0, this constrains lhs * rhs = out.
vec![s_mul * (lhs * rhs  out)]
});
FieldConfig {
advice,
instance,
s_mul,
constant,
}
}
}
Implement chip traits
/// A variable representing a number.
#[derive(Clone)]
struct Number<F: FieldExt> {
cell: Cell,
value: Option<F>,
}
impl<F: FieldExt> NumericInstructions<F> for FieldChip<F> {
type Num = Number<F>;
fn load_private(
&self,
mut layouter: impl Layouter<F>,
value: Option<F>,
) > Result<Self::Num, Error> {
let config = self.config();
let mut num = None;
layouter.assign_region(
 "load private",
mut region {
let cell = region.assign_advice(
 "private input",
config.advice[0],
0,
 value.ok_or(Error::Synthesis),
)?;
num = Some(Number { cell, value });
Ok(())
},
)?;
Ok(num.unwrap())
}
fn load_constant(
&self,
mut layouter: impl Layouter<F>,
constant: F,
) > Result<Self::Num, Error> {
let config = self.config();
let mut num = None;
layouter.assign_region(
 "load constant",
mut region {
let cell = region.assign_advice_from_constant(
 "constant value",
config.advice[0],
0,
constant,
)?;
num = Some(Number {
cell,
value: Some(constant),
});
Ok(())
},
)?;
Ok(num.unwrap())
}
fn mul(
&self,
mut layouter: impl Layouter<F>,
a: Self::Num,
b: Self::Num,
) > Result<Self::Num, Error> {
let config = self.config();
let mut out = None;
layouter.assign_region(
 "mul",
mut region: Region<'_, F> {
// We only want to use a single multiplication gate in this region,
// so we enable it at region offset 0; this means it will constrain
// cells at offsets 0 and 1.
config.s_mul.enable(&mut region, 0)?;
// The inputs we've been given could be located anywhere in the circuit,
// but we can only rely on relative offsets inside this region. So we
// assign new cells inside the region and constrain them to have the
// same values as the inputs.
let lhs = region.assign_advice(
 "lhs",
config.advice[0],
0,
 a.value.ok_or(Error::Synthesis),
)?;
let rhs = region.assign_advice(
 "rhs",
config.advice[1],
0,
 b.value.ok_or(Error::Synthesis),
)?;
region.constrain_equal(a.cell, lhs)?;
region.constrain_equal(b.cell, rhs)?;
// Now we can assign the multiplication result into the output position.
let value = a.value.and_then(a b.value.map(b a * b));
let cell = region.assign_advice(
 "lhs * rhs",
config.advice[0],
1,
 value.ok_or(Error::Synthesis),
)?;
// Finally, we return a variable representing the output,
// to be used in another part of the circuit.
out = Some(Number { cell, value });
Ok(())
},
)?;
Ok(out.unwrap())
}
fn expose_public(
&self,
mut layouter: impl Layouter<F>,
num: Self::Num,
row: usize,
) > Result<(), Error> {
let config = self.config();
layouter.constrain_instance(num.cell, config.instance, row)
}
}
Build the circuit
Now that we have the instructions we need, and a chip that implements them, we can finally build our circuit!
/// The full circuit implementation.
///
/// In this struct we store the private input variables. We use `Option<F>` because
/// they won't have any value during key generation. During proving, if any of these
/// were `None` we would get an error.
#[derive(Default)]
struct MyCircuit<F: FieldExt> {
constant: F,
a: Option<F>,
b: Option<F>,
}
impl<F: FieldExt> Circuit<F> for MyCircuit<F> {
// Since we are using a single chip for everything, we can just reuse its config.
type Config = FieldConfig;
type FloorPlanner = SimpleFloorPlanner;
fn without_witnesses(&self) > Self {
Self::default()
}
fn configure(meta: &mut ConstraintSystem<F>) > Self::Config {
// We create the two advice columns that FieldChip uses for I/O.
let advice = [meta.advice_column(), meta.advice_column()];
// We also need an instance column to store public inputs.
let instance = meta.instance_column();
// Create a fixed column to load constants.
let constant = meta.fixed_column();
FieldChip::configure(meta, advice, instance, constant)
}
fn synthesize(
&self,
config: Self::Config,
mut layouter: impl Layouter<F>,
) > Result<(), Error> {
let field_chip = FieldChip::<F>::construct(config);
// Load our private values into the circuit.
let a = field_chip.load_private(layouter.namespace( "load a"), self.a)?;
let b = field_chip.load_private(layouter.namespace( "load b"), self.b)?;
// Load the constant factor into the circuit.
let constant =
field_chip.load_constant(layouter.namespace( "load constant"), self.constant)?;
// We only have access to plain multiplication.
// We could implement our circuit as:
// asq = a*a
// bsq = b*b
// absq = asq*bsq
// c = constant*asq*bsq
//
// but it's more efficient to implement it as:
// ab = a*b
// absq = ab^2
// c = constant*absq
let ab = field_chip.mul(layouter.namespace( "a * b"), a, b)?;
let absq = field_chip.mul(layouter.namespace( "ab * ab"), ab.clone(), ab)?;
let c = field_chip.mul(layouter.namespace( "constant * absq"), constant, absq)?;
// Expose the result as a public input to the circuit.
field_chip.expose_public(layouter.namespace( "expose c"), c, 0)
}
}
Testing the circuit
halo2::dev::MockProver
can be used to test that the circuit is working correctly. The
private and public inputs to the circuit are constructed as we will do to create a proof,
but by passing them to MockProver::run
we get an object that can test every constraint
in the circuit, and tell us exactly what is failing (if anything).
// The number of rows in our circuit cannot exceed 2^k. Since our example
// circuit is very small, we can pick a very small value here.
let k = 4;
// Prepare the private and public inputs to the circuit!
let constant = Fp::from(7);
let a = Fp::from(2);
let b = Fp::from(3);
let c = constant * a.square() * b.square();
// Instantiate the circuit with the private inputs.
let circuit = MyCircuit {
constant,
a: Some(a),
b: Some(b),
};
// Arrange the public input. We expose the multiplication result in row 0
// of the instance column, so we position it there in our public inputs.
let mut public_inputs = vec![c];
// Given the correct public input, our circuit will verify.
let prover = MockProver::run(k, &circuit, vec![public_inputs.clone()]).unwrap();
assert_eq!(prover.verify(), Ok(()));
// If we try some other public input, the proof will fail!
public_inputs[0] += Fp::one();
let prover = MockProver::run(k, &circuit, vec![public_inputs]).unwrap();
assert!(prover.verify().is_err());
Full example
You can find the source code for this example here.
Lookup tables
In normal programs, you can trade memory for CPU to improve performance, by precomputing and storing lookup tables for some part of the computation. We can do the same thing in halo2 circuits!
A lookup table can be thought of as enforcing a relation between variables, where the relation is expressed as a table. Assuming we have only one lookup argument in our constraint system, the total size of tables is constrained by the size of the circuit: each table entry costs one row, and it also costs one row to do each lookup.
TODO
Gadgets
Tips and tricks
This section contains various ideas and snippets that you might find useful while writing halo2 circuits.
Small range constraints
A common constraint used in R1CS circuits is the boolean constraint: $b∗(1−b)=0$. This constraint can only be satisfied by $b=0$ or $b=1$.
In halo2 circuits, you can similarly constrain a cell to have one of a small set of values. For example, to constrain $a$ to the range $[0..5]$, you would create a gate of the form:
$a⋅(1−a)⋅(2−a)⋅(3−a)⋅(4−a)=0$
while to constraint $c$ to be either 7 or 13, you would use:
$(7−c)⋅(13−c)=0$
The underlying principle here is that we create a polynomial constraint with roots at each value in the set of possible values we want to allow. In R1CS circuits, the maximum supported polynomial degree is 2 (due to all constraints being of the form $a∗b=c$). In halo2 circuits, you can use arbitrarydegree polynomials  with the proviso that higherdegree constraints are more expensive to use.
Note that the roots don't have to be constants; for example $(a−x)⋅(a−y)⋅(a−z)=0$ will constrain $a$ to be equal to one of ${x,y,z}$ where the latter can be arbitrary polynomials, as long as the whole expression stays within the maximum degree bound.
Small set interpolation
We can use Lagrange interpolation to create a polynomial constraint that maps $f(X)=Y$ for small sets of $X∈{x_{i}},Y∈{y_{i}}$.
For instance, say we want to map a 2bit value to a "spread" version interleaved with zeros. We first precompute the evaluations at each point:
$00→000001→000110→010011→0101 ⟹⟹⟹⟹ 0→01→12→43→5 $
Then, we construct the Lagrange basis polynomial for each point using the identity: $l_{j}(X)=0≤m<k,m=j∏ x_{j}−x_{m}x−x_{m} ,$ where $k$ is the number of data points. ($k=4$ in our example above.)
Recall that the Lagrange basis polynomial $l_{j}(X)$ evaluates to $1$ at $X=x_{j}$ and $0$ at all other $x_{i},j=i.$
Continuing our example, we get four Lagrange basis polynomials:
$l_{0}(X)l_{1}(X)l_{2}(X)l_{3}(X) ==== (−3)(−2)(−1)(X−3)(X−2)(X−1) (−2)(−1)(1)(X−3)(X−2)(X) (−1)(1)(2)(X−3)(X−1)(X) (1)(2)(3)(X−2)(X−1)(X) $
Our polynomial constraint is then
$⟹ f(0)⋅l_{0}(X)0⋅l_{0}(X) ++ f(1)⋅l_{1}(X)1⋅l_{1}(X) ++ f(2)⋅l_{2}(X)4⋅l_{2}(X) ++ f(3)⋅l_{3}(X)5⋅l_{3}(X) −− f(X)f(X) == 00. $
Design
Note on Language
We use slightly different language than others to describe PLONK concepts. Here's the overview:
 We like to think of PLONKlike arguments as tables, where each column corresponds to a "wire". We refer to entries in this table as "cells".
 We like to call "selector polynomials" and so on "fixed columns" instead. We then refer specifically to a "selector constraint" when a cell in a fixed column is being used to control whether a particular constraint is enabled in that row.
 We call the other polynomials "advice columns" usually, when they're populated by the prover.
 We use the term "rule" to refer to a "gate" like
$A(X)⋅q_{A}(X)+B(X)⋅q_{B}(X)+A(X)⋅B(X)⋅q_{M}(X)+C(X)⋅q_{C}(X)=0.$
 TODO: Check how consistent we are with this, and update the code and docs to match.
Proving system
The Halo 2 proving system can be broken down into five stages:
 Commit to polynomials encoding the main components of the circuit:
 Cell assignments.
 Permuted values and products for each lookup argument.
 Equality constraint permutations.
 Construct the vanishing argument to constrain all circuit relations to zero:
 Standard and custom gates.
 Lookup argument rules.
 Equality constraint permutation rules.
 Evaluate the above polynomials at all necessary points:
 All relative rotations used by custom gates across all columns.
 Vanishing argument pieces.
 Construct the multipoint opening argument to check that all evaluations are consistent with their respective commitments.
 Run the inner product argument to create a polynomial commitment opening proof for the multipoint opening argument polynomial.
These stages are presented in turn across this section of the book.
Example
To aid our explanations, we will at times refer to the following example constraint system:
 Four advice columns $a,b,c,d$.
 One fixed column $f$.
 Three custom gates:
 $a⋅b⋅c_{−1}−d=0$
 $f_{−1}⋅c=0$
 $f⋅d⋅a=0$
tl;dr
The table below provides a (probably too) succinct description of the Halo 2 protocol. This description will likely be replaced by the Halo 2 paper and security proof, but for now serves as a summary of the following subsections.
Prover  Verifier  

$←$  $t(X)=(X_{n}−1)$  
$←$  $F=[F_{0},F_{1},…,F_{m−1}]$  
$A=[A_{0},A_{1},…,A_{m−1}]$  $→$  
$←$  $θ$  
$L=[(A_{0},S_{0}),…,(A_{m−1},S_{m−1})]$  $→$  
$←$  $β,γ$  
$Z_{P}=[Z_{P,0},Z_{P,1},…]$  $→$  
$Z_{L}=[Z_{L,0},Z_{L,1},…]$  $→$  
$←$  $y$  
$h(X)=t(X)gate_{0}(X)+⋯+y_{i}⋅gate_{i}(X) $  
$h(X)=h_{0}(X)+⋯+X_{n(d−1)}h_{d−1}(X)$  
$H=[H_{0},H_{1},…,H_{d−1}]$  $→$  
$←$  $x$  
$evals=[A_{0}(x),…,H_{d−1}(x)]$  $→$  
Checks $h(x)$  
$←$  $x_{1},x_{2}$  
Constructs $h_{′}(X)$ multipoint opening poly  
$U=Commit(h_{′}(X))$  $→$  
$←$  $x_{3}$  
$q_{evals}=[Q_{0}(x_{3}),Q_{1}(x_{3}),…]$  $→$  
$u_{eval}=U(x_{3})$  $→$  
$←$  $x_{4}$ 
Then the prover and verifier:
 Construct $finalPoly(X)$ as a linear combination of $Q$ and $U$ using powers of $x_{4}$;
 Construct $finalPolyEval$ as the equivalent linear combination of $q_{evals}$ and $u_{eval}$; and
 Perform $InnerProduct(finalPoly(X),x_{3},finalPolyEval).$
TODO: Write up protocol components that provide zeroknowledge.
Lookup argument
halo2 uses the following lookup technique, which allows for lookups in arbitrary sets, and is arguably simpler than Plookup.
Note on Language
In addition to the general notes on language:
 We call the $Z(X)$ polynomial (the grand product argument polynomial for the permutation argument) the "permutation product" column.
Technique Description
For ease of explanation, we'll first describe a simplified version of the argument that ignores zero knowledge.
We express lookups in terms of a "subset argument" over a table with $2_{k}$ rows (numbered from 0), and columns $A$ and $S.$
The goal of the subset argument is to enforce that every cell in $A$ is equal to some cell in $S.$ This means that more than one cell in $A$ can be equal to the same cell in $S,$ and some cells in $S$ don't need to be equal to any of the cells in $A.$
 $S$ might be fixed, but it doesn't need to be. That is, we can support looking up values in either fixed or variable tables (where the latter includes advice columns).
 $A$ and $S$ can contain duplicates. If the sets represented by $A$ and/or $S$ are not
naturally of size $2_{k},$ we extend $S$ with duplicates and $A$ with dummy values known
to be in $S.$
 Alternatively we could add a "lookup selector" that controls which elements of the $A$ column participate in lookups. This would modify the occurrence of $A(X)$ in the permutation rule below to replace $A$ with, say, $S_{0}$ if a lookup is not selected.
Let $ℓ_{i}$ be the Lagrange basis polynomial that evaluates to $1$ at row $i,$ and $0$ otherwise.
We start by allowing the prover to supply permutation columns of $A$ and $S.$ Let's call these $A_{′}$ and $S_{′},$ respectively. We can enforce that they are permutations using a permutation argument with product column $Z$ with the rules:
$Z(ωX)⋅(A_{′}(X)+β)⋅(S_{′}(X)+γ)−Z(X)⋅(A(X)+β)⋅(S(X)+γ)=0$$ℓ_{0}(X)⋅(1−Z(X))=0$
i.e. provided that division by zero does not occur, we have for all $i∈[0,2_{k})$:
$Z_{i+1}=Z_{i}⋅(A_{i}+β)⋅(S_{i}+γ)(A_{i}+β)⋅(S_{i}+γ) $$Z_{2_{k}}=Z_{0}=1.$
This is a version of the permutation argument which allows $A_{′}$ and $S_{′}$ to be permutations of $A$ and $S,$ respectively, but doesn't specify the exact permutations. $β$ and $γ$ are separate challenges so that we can combine these two permutation arguments into one without worrying that they might interfere with each other.
The goal of these permutations is to allow $A_{′}$ and $S_{′}$ to be arranged by the prover in a particular way:
 All the cells of column $A_{′}$ are arranged so that likevalued cells are vertically adjacent to each other. This could be done by some kind of sorting algorithm, but all that matters is that likevalued cells are on consecutive rows in column $A_{′},$ and that $A_{′}$ is a permutation of $A.$
 The first row in a sequence of like values in $A_{′}$ is the row that has the corresponding value in $S_{′}.$ Apart from this constraint, $S_{′}$ is any arbitrary permutation of $S.$
Now, we'll enforce that either $A_{i}=S_{i}$ or that $A_{i}=A_{i−1},$ using the rule
$(A_{′}(X)−S_{′}(X))⋅(A_{′}(X)−A_{′}(ω_{−1}X))=0$
In addition, we enforce $A_{0}=S_{0}$ using the rule
$ℓ_{0}(X)⋅(A_{′}(X)−S_{′}(X))=0$
(The $A_{′}(X)−A_{′}(ω_{−1}X)$ term of the first rule here has no effect at row $0,$ even though $ω_{−1}X$ "wraps", because of the second rule.)
Together these constraints effectively force every element in $A_{′}$ (and thus $A$) to equal at least one element in $S_{′}$ (and thus $S$). Proof: by induction on prefixes of the rows.
Zeroknowledge adjustment
In order to achieve zero knowledge for the PLONKbased proof system, we will need the last $t$ rows of each column to be filled with random values. This requires an adjustment to the lookup argument, because these random values would not satisfy the constraints described above.
We limit the number of usable rows to $u=2_{k}−t−1.$ We add two selectors:
 $q_{blind}$ is set to $1$ on the last $t$ rows, and $0$ elsewhere;
 $q_{last}$ is set to $1$ only on row $u,$ and $0$ elsewhere (i.e. it is set on the row in between the usable rows and the blinding rows).
We enable the constraints from above only for the usable rows:
$(1−(q_{last}(X)+q_{blind}(X)))⋅(Z(ωX)⋅(A_{′}(X)+β)⋅(S_{′}(X)+γ)−Z(X)⋅(A(X)+β)⋅(S(X)+γ))=0$$(1−(q_{last}(X)+q_{blind}(X)))⋅(A_{′}(X)−S_{′}(X))⋅(A_{′}(X)−A_{′}(ω_{−1}X))=0$
The rules that are enabled on row $0$ remain the same:
$ℓ_{0}(X)⋅(A_{′}(X)−S_{′}(X))=0$$ℓ_{0}(X)⋅(1−Z(X))=0$
Since we can no longer rely on the wraparound to ensure that the product $Z$ becomes $1$ again at $ω_{2_{k}},$ we would instead need to constrain $Z(ω_{u})$ to $1.$ However, there is a potential difficulty: if any of the values $A_{i}+β$ or $S_{i}+γ$ are zero for $i∈[0,u),$ then it might not be possible to satisfy the permutation argument. This occurs with negligble probability over choices of $β$ and $γ,$ but is an obstacle to achieving perfect zero knowledge (because an adversary can rule out witnesses that would cause this situation), as well as perfect completeness.
To ensure both perfect completeness and perfect zero knowledge, we allow $Z(ω_{u})$ to be either zero or one:
$q_{last}(X)⋅(Z(X)_{2}−Z(X))=0$
Now if $A_{i}+β$ or $S_{i}+γ$ are zero for some $i,$ we can set $Z_{j}=0$ for $i<j≤u,$ satisfying the constraint system.
Note that the challenges $β$ and $γ$ are chosen after committing to $A$ and $S$ (and to $A_{′}$ and $S_{′}$), so the prover cannot force the case where some $A_{i}+β$ or $S_{i}+γ$ is zero to occur. Since this case occurs with negligible probability, soundness is not affected.
Cost
 There is the original column $A$ and the fixed column $S.$
 There is a permutation product column $Z.$
 There are the two permutations $A_{′}$ and $S_{′}.$
 The gates are all of low degree.
Generalizations
halo2's lookup argument implementation generalizes the above technique in the following ways:
 $A$ and $S$ can be extended to multiple columns, combined using a random challenge. $A_{′}$
and $S_{′}$ stay as single columns.
 The commitments to the columns of $S$ can be precomputed, then combined cheaply once the challenge is known by taking advantage of the homomorphic property of Pedersen commitments.
 The columns of $A$ can be given as arbitrary polynomial expressions using relative references. These will be substituted into the product column constraint, subject to the maximum degree bound. This potentially saves one or more advice columns.
 Then, a lookup argument for an arbitrarywidth relation can be implemented in terms of a
subset argument, i.e. to constrain $R(x,y,...)$ in each row, consider
$R$ as a set of tuples $S$ (using the method of the previous point), and check
that $(x,y,...)∈R.$
 In the case where $R$ represents a function, this implicitly also checks that the inputs are in the domain. This is typically what we want, and often saves an additional range check.
 We can support multiple tables in the same circuit, by combining them into a single
table that includes a tag column to identify the original table.
 The tag column could be merged with the "lookup selector" mentioned earlier, if this were implemented.
These generalizations are similar to those in sections 4 and 5 of the Plookup paper. That is, the differences from Plookup are in the subset argument. This argument can then be used in all the same ways; for instance, the optimized range check technique in section 5 of the Plookup paper can also be used with this subset argument.
Permutation argument
Given that gates in halo2 circuits operate "locally" (on cells in the current row or defined relative rows), it is common to need to copy a value from some arbitrary cell into the current row for use in a gate. This is performed with an equality constraint, which enforces that the source and destination cells contain the same value.
We implement these equality constraints by constructing a permutation that represents the constraints, and then using a permutation argument within the proof to enforce them.
Notation
A permutation is a onetoone and onto mapping of a set onto itself. A permutation can be factored uniquely into a composition of cycles (up to ordering of cycles, and rotation of each cycle).
We sometimes use cycle notation to write permutations. Let $(abc)$ denote a cycle where $a$ maps to $b,$ $b$ maps to $c,$ and $c$ maps to $a$ (with the obvious generalization to arbitrarysized cycles). Writing two or more cycles next to each other denotes a composition of the corresponding permutations. For example, $(ab)(cd)$ denotes the permutation that maps $a$ to $b,$ $b$ to $a,$ $c$ to $d,$ and $d$ to $c.$
Constructing the permutation
Goal
We want to construct a permutation in which each subset of variables that are in a equalityconstraint set form a cycle. For example, suppose that we have a circuit that defines the following equality constraints:
 $a≡b$
 $a≡c$
 $d≡e$
From this we have the equalityconstraint sets ${a,b,c}$ and ${d,e}.$ We want to construct the permutation:
$(abc)(de)$
which defines the mapping of $[a,b,c,d,e]$ to $[b,c,a,e,d].$
Algorithm
We need to keep track of the set of cycles, which is a set of disjoint sets. Efficient data structures for this problem are known; for the sake of simplicity we choose one that is not asymptotically optimal but is easy to implement.
We represent the current state as:
 an array $mapping$ for the permutation itself;
 an auxiliary array $aux$ that keeps track of a distinguished element of each cycle;
 another array $sizes$ that keeps track of the size of each cycle.
We have the invariant that for each element $x$ in a given cycle $C,$ $aux(x)$ points to the same element $c∈C.$ This allows us to quickly decide whether two given elements $x$ and $y$ are in the same cycle, by checking whether $aux(x)=aux(y).$ Also, $sizes(aux(x))$ gives the size of the cycle containing $x.$ (This is guaranteed only for $sizes(aux(x)),$ not for $sizes(x).$)
The algorithm starts with a representation of the identity permutation: for all $x,$ we set $mapping(x)=x,$ $aux(x)=x,$ and $sizes(x)=1.$
To add an equality constraint $left≡right$:
 Check whether $left$ and $right$ are already in the same cycle, i.e. whether $aux(left)=aux(right).$ If so, there is nothing to do.
 Otherwise, $left$ and $right$ belong to different cycles. Make $left$ the larger cycle and $right$ the smaller one, by swapping them iff $sizes(aux(left))<sizes(aux(right)).$
 Set $sizes(aux(left)):=sizes(aux(left))+sizes(aux(right)).$
 Following the mapping around the right (smaller) cycle, for each element $x$ set $aux(x):=aux(left).$
 Splice the smaller cycle into the larger one by swapping $mapping(left)$ with $mapping(right).$
For example, given two disjoint cycles $(ABCD)$ and $(EFGH)$:
A +> B
^ +
 
+ v
D <+ C E +> F
^ +
 
+ v
H <+ G
After adding constraint $B≡E$ the above algorithm produces the cycle:
A +> B ++
^ 
 
+ v
D <+ C <+ E F
^ +
 
+ v
H <+ G
Broken alternatives
If we did not check whether $left$ and $right$ were already in the same cycle, then we could end up undoing an equality constraint. For example, if we have the following constraints:
 $a≡b$
 $b≡c$
 $c≡d$
 $b≡d$
and we tried to implement adding an equality constraint just using step 5 of the above algorithm, then we would end up constructing the cycle $(ab)(cd),$ rather than the correct $(abcd).$
Argument specification
We need to check a permutation of cells in $m$ columns, represented in Lagrange basis by polynomials $v_{0},…,v_{m−1}.$
We will label each cell in those $m$ columns with a unique element of $F_{×}.$
Suppose that we have a permutation on these labels, $σ(column:i,row:j)=(column:i_{′},row:j_{′}).$ in which the cycles correspond to equalityconstraint sets.
If we consider the set of pairs ${(label,value)}$, then the values within each cycle are equal if and only if permuting the label in each pair by $σ$ yields the same set:
Since the labels are distinct, set equality is the same as multiset equality, which we can check using a product argument.
Let $ω$ be a $2_{k}$ root of unity and let $δ$ be a $T$ root of unity, where $T⋅2_{S}+1=p$ with $T$ odd and $k≤S.$ We will use $δ_{i}⋅ω_{j}∈F_{×}$ as the label for the cell in the $j$th row of the $i$th column of the permutation argument.
We represent $σ$ by a vector of $m$ polynomials $s_{i}(X)$ such that $s_{i}(ω_{j})=δ_{i_{′}}⋅ω_{j_{′}}.$
Notice that the identity permutation can be represented by the vector of $m$ polynomials $ID_{i}(ω_{j})$ such that $ID_{i}(ω_{j})=δ_{i}⋅ω_{j}.$
We will use a challenge $β$ to compress each $(label,value)$ pair to $value+β⋅label.$ Just as in the product argument we used for lookups, we also use a challenge $γ$ to randomize each term of the product.
Now given our permutation represented by $s_{0},…,s_{m−1}$ over columns represented by $v_{0},…,v_{m−1},$ we want to ensure that: $i=0∏m−1 j=0∏n−1 (v_{i}(ω_{j})+β⋅s_{i}(ω_{j})+γv_{i}(ω_{j})+β⋅δ_{i}⋅ω_{j}+γ )=1$
Here $v_{i}(ω_{j})+β⋅δ_{i}⋅ω_{j}$ represents the unpermuted $(label,value)$ pair, and $v_{i}(ω_{j})+β⋅s_{i}(ω_{j})$ represents the permuted $(σ(label),value)$ pair.
Let $Z_{P}$ be such that $Z_{P}(ω_{0})=Z_{P}(ω_{n})=1$ and for $0≤j<n$: $Z_{P}(ω_{j+1}) =h=0∏j i=0∏m−1 v_{i}(ω_{h})+β⋅s_{i}(ω_{h})+γv_{i}(ω_{h})+β⋅δ_{i}⋅ω_{h}+γ =Z_{P}(ω_{j})i=0∏m−1 v_{i}(ω_{j})+β⋅s_{i}(ω_{j})+γv_{i}(ω_{j})+β⋅δ_{i}⋅ω_{j}+γ $
Then it is sufficient to enforce the rules: $Z_{P}(ωX)⋅i=0∏m−1 (v_{i}(X)+β⋅s_{i}(X)+γ)−Z_{P}(X)⋅i=0∏m−1 (v_{i}(X)+β⋅δ_{i}⋅X+γ)=0ℓ_{0}⋅(1−Z_{P}(X))=0$
This assumes that the number of columns $m$ is such that the polynomial in the first rule above fits within the degree bound of the PLONK configuration. We will see below how to handle a larger number of columns.
The optimization used to obtain the simple representation of the identity permutation was suggested by Vitalik Buterin for PLONK, and is described at the end of section 8 of the PLONK paper. Note that the $δ_{i}$ are all distinct quadratic nonresidues, provided that the number of columns that are enabled for equality is no more than $T$, which always holds in practice for the curves used in Halo 2.
Zeroknowledge adjustment
Similarly to the lookup argument, we need an adjustment to the above argument to account for the last $t$ rows of each column being filled with random values.
We limit the number of usable rows to $u=2_{k}−t−1.$ We add two selectors, defined in the same way as for the lookup argument:
 $q_{blind}$ is set to $1$ on the last $t$ rows, and $0$ elsewhere;
 $q_{last}$ is set to $1$ only on row $u,$ and $0$ elsewhere (i.e. it is set on the row in between the usable rows and the blinding rows).
We enable the product rule from above only for the usable rows:
$(1−(q_{last}(X)+q_{blind}(X)))⋅$ $(Z_{P}(ωX)⋅i=0∏m−1 (v_{i}(X)+β⋅s_{i}(X)+γ)−Z_{P}(X)⋅i=0∏m−1 (v_{i}(X)+β⋅δ_{i}⋅X+γ))=0$
The rule that is enabled on row $0$ remains the same:
$ℓ_{0}(X)⋅(1−Z_{P}(X))=0$
Since we can no longer rely on the wraparound to ensure that each product $Z_{P}$ becomes $1$ again at $ω_{2_{k}},$ we would instead need to constrain $Z(ω_{u})=1.$ This raises the same problem that was described for the lookup argument. So we allow $Z(ω_{u})$ to be either zero or one:
$q_{last}(X)⋅(Z_{P}(X)_{2}−Z_{P}(X))=0$
which gives perfect completeness and zero knowledge.
Spanning a large number of columns
The halo2 implementation does not in practice limit the number of columns for which equality constraints can be enabled. Therefore, it must solve the problem that the above approach might yield a product rule with a polynomial that exceeds the PLONK configuration's degree bound. The degree bound could be raised, but this would be inefficient if no other rules require a larger degree.
Instead, we split the product across $b$ sets of $m$ columns, using product columns $Z_{P,0},…Z_{P,b−1},$ and we use another rule to copy the product from the end of one column set to the beginning of the next.
That is, for $0≤a<b$ we have:
$(1−(q_{last}(X)+q_{blind}(X)))⋅$ $(Z_{P,a}(ωX)⋅i=am∏(a+1)m−1 (v_{i}(X)+β⋅s_{i}(X)+γ)−Z_{P}(X)⋅i=am∏(a+1)m−1 (v_{i}(X)+β⋅δ_{i}⋅X+γ))$ $=0$
For simplicity this is written assuming that the number of columns enabled for equality constraints is a multiple of $m$; if not then the products for the last column set will have fewer than $m$ terms.
For the first column set we have:
$ℓ_{0}⋅(1−Z_{P,0}(X))=0$
For each subsequent column set, $0<a<b,$ we use the following rule to copy $Z_{P,a−1}(ω_{u})$ to the start of the next column set, $Z_{P,a}(ω_{0})$:
$ℓ_{0}⋅(Z_{P,a}(X)−Z_{P,a−1}(ω_{u}X))=0$
For the last column set, we allow $Z_{P,b−1}(ω_{u})$ to be either zero or one:
$q_{last}(X)⋅(Z_{P,b−1}(X)_{2}−Z_{P,b−1}(X))=0$
which gives perfect completeness and zero knowledge as before.
Circuit commitments
Committing to the circuit assignments
At the start of proof creation, the prover has a table of cell assignments that it claims satisfy the constraint system. The table has $n=2_{k}$ rows, and is broken into advice, instance, and fixed columns. We define $F_{i,j}$ as the assignment in the $j$th row of the $i$th fixed column. Without loss of generality, we'll similarly define $A_{i,j}$ to represent the advice and instance assignments.
We separate fixed columns here because they are provided by the verifier, whereas the advice and instance columns are provided by the prover. In practice, the commitments to instance and fixed columns are computed by both the prover and verifier, and only the advice commitments are stored in the proof.
To commit to these assignments, we construct Lagrange polynomials of degree $n−1$ for each column, over an evaluation domain of size $n$ (where $ω$ is the $n$th primitive root of unity):
 $a_{i}(X)$ interpolates such that $a_{i}(ω_{j})=A_{i,j}$.
 $f_{i}(X)$ interpolates such that $f_{i}(ω_{j})=F_{i,j}$.
We then create a blinding commitment to the polynomial for each column:
$A=[Commit(a_{0}(X)),…,Commit(a_{i}(X))]$ $F=[Commit(f_{0}(X)),…,Commit(f_{i}(X))]$
$F$ is constructed as part of key generation, using a blinding factor of $1$. $A$ is constructed by the prover and sent to the verifier.
Committing to the lookup permutations
The verifier starts by sampling $θ$, which is used to keep individual columns within lookups independent. Then, the prover commits to the permutations for each lookup as follows:

Given a lookup with input column polynomials $[A_{0}(X),…,A_{m−1}(X)]$ and table column polynomials $[S_{0}(X),…,S_{m−1}(X)]$, the prover constructs two compressed polynomials
$A_{compressed}(X)=θ_{m−1}A_{0}(X)+θ_{m−2}A_{1}(X)+⋯+θA_{m−2}(X)+A_{m−1}(X)$ $S_{compressed}(X)=θ_{m−1}S_{0}(X)+θ_{m−2}S_{1}(X)+⋯+θS_{m−2}(X)+S_{m−1}(X)$

The prover then permutes $A_{compressed}(X)$ and $S_{compressed}(X)$ according to the rules of the lookup argument, obtaining $A_{′}(X)$ and $S_{′}(X)$.
The prover creates blinding commitments for all of the lookups
$L=[(Commit(A_{′}(X))),Commit(S_{′}(X))),…]$
and sends them to the verifier.
After the verifier receives $A$, $F$, and $L$, it samples challenges $β$ and $γ$ that will be used in the permutation argument and the remainder of the lookup argument below. (These challenges can be reused because the arguments are independent.)
Committing to the equality constraint permutation
Let $c$ be the number of columns that are enabled for equality constraints.
Let $m$ be the maximum number of columns that can accomodated by a column set without exceeding the PLONK configuration's polynomial degree bound.
Let $u$ be the number of “usable” rows as defined in the Permutation argument section.
Let $b=ceiling(c/m).$
The prover constructs a vector $P$ of length $bu$ such that for each column set $0≤a<b$ and each row $0≤j<u,$
$P_{au+j}=i=am∏min(c,(a+1)m)−1 v_{i}(ω_{j})+β⋅s_{i}(ω_{j})+γv_{i}(ω_{j})+β⋅δ_{i}⋅ω_{j}+γ .$
The prover then computes a running product of $P$, starting at $1$, and a vector of polynomials $Z_{P,0..b−1}$ that each have a Lagrange basis representation corresponding to a $u$sized slice of this running product, as described in the Permutation argument section.
The prover creates blinding commitments to each $Z_{P,a}$ polynomial:
$Z_{P}=[Commit(Z_{P,0}(X)),…,Commit(Z_{P,b−1}(X))]$
and sends them to the verifier.
Committing to the lookup permutation product columns
In addition to committing to the individual permuted lookups, for each lookup, the prover needs to commit to the permutation product column:
 The prover constructs a vector $P$:
$P_{j}=(A_{′}(ω_{j})+β)(S_{′}(ω_{j})+γ)(A_{compressed}(ω_{j})+β)(S_{compressed}(ω_{j})+γ) $
 The prover constructs a polynomial $Z_{L}$ which has a Lagrange basis representation corresponding to a running product of $P$, starting at $Z_{L}(1)=1$.
$β$ and $γ$ are used to combine the permutation arguments for $A_{′}(X)$ and $S_{′}(X)$ while keeping them independent. The important thing here is that the verifier samples $β$ and $γ$ after the prover has created $A$, $F$, and $L$ (and thus committed to all the cell values used in lookup columns, as well as $A_{′}(X)$ and $S_{′}(X)$ for each lookup).
As before, the prover creates blinding commitments to each $Z_{L}$ polynomial:
$Z_{L}=[Commit(Z_{L}(X)),…]$
and sends them to the verifier.
Vanishing argument
Having committed to the circuit assignments, the prover now needs to demonstrate that the various circuit relations are satisfied:
 The custom gates, represented by polynomials $gate_{i}(X)$.
 The rules of the lookup arguments.
 The rules of the equality constraint permutations.
Each of these relations is represented as a polynomial of degree $d$ (the maximum degree of any of the relations) with respect to the circuit columns. Given that the degree of the assignment polynomials for each column is $n−1$, the relation polynomials have degree $d(n−1)$ with respect to $X$.
In our example, these would be the gate polynomials, of degree $3n−3$:
 $gate_{0}(X)=a_{0}(X)⋅a_{1}(X)⋅a_{2}(Xω_{−1})−a_{3}(X)$
 $gate_{1}(X)=f_{0}(Xω_{−1})⋅a_{2}(X)$
 $gate_{2}(X)=f_{0}(X)⋅a_{3}(X)⋅a_{0}(X)$
A relation is satisfied if its polynomial is equal to zero. One way to demonstrate this is to divide each polynomial relation by the vanishing polynomial $t(X)=(X_{n}−1)$, which is the lowestdegree monomial that has roots at every $ω_{i}$. If relation's polynomial is perfectly divisible by $t(X)$, it is equal to zero over the domain (as desired).
This simple construction would require a polynomial commitment per relation. Instead, we commit to all of the circuit relations simultaneously: the verifier samples $y$, and then the prover constructs the quotient polynomial
$h(X)=t(X)gate_{0}(X)+y⋅gate_{1}(X)+⋯+y_{i}⋅gate_{i}(X)+… ,$
where the numerator is a random (the prover commits to the cell assignments before the verifier samples $y$) linear combination of the circuit relations.
 If the numerator polynomial (in formal indeterminate $X$) is perfectly divisible by $t(X)$, then with high probability all relations are satisfied.
 Conversely, if at least one relation is not satisfied, then with high probability $h(x)⋅t(x)$ will not equal the evaluation of the numerator at $x$. In this case, the numerator polynomial would not be perfectly divisible by $t(X)$.
Committing to $h(X)$
$h(X)$ has degree $(d−1)n−d$ (because the divisor $t(X)$ has degree $n$). However, the polynomial commitment scheme we use for Halo 2 only supports committing to polynomials of degree $n−1$ (which is the maximum degree that the rest of the protocol needs to commit to). Instead of increasing the cost of the polynomial commitment scheme, the prover split $h(X)$ into pieces of degree $n−1$
$h_{0}(X)+X_{n}h_{1}(X)+⋯+X_{n(d−1)}h_{d−1}(X),$
and produces blinding commitments to each piece
$H=[Commit(h_{0}(X)),Commit(h_{1}(X)),…,Commit(h_{d−1}(X))].$
Evaluating the polynomials
At this point, all properties of the circuit have been committed to. The verifier now wants to see if the prover committed to the correct $h(X)$ polynomial. The verifier samples $x$, and the prover produces the purported evaluations of the various polynomials at $x$, for all the relative offsets used in the circuit, as well as $h(X)$.
In our example, this would be:
 $a_{0}(x)$
 $a_{1}(x)$
 $a_{2}(x)$, $a_{2}(xω_{−1})$
 $a_{3}(x)$
 $f_{0}(x)$, $f_{0}(xω_{−1})$
 $h_{0}(x)$, ..., $h_{d−1}(x)$
The verifier checks that these evaluations satisfy the form of $h(X)$:
$t(x)gate_{0}(x)+⋯+y_{i}⋅gate_{i}(x)+… =h_{0}(x)+⋯+x_{n(d−1)}h_{d−1}(x)$
Now content that the evaluations collectively satisfy the gate constraints, the verifier needs to check that the evaluations themselves are consistent with the original circuit commitments, as well as $H$. To implement this efficiently, we use a multipoint opening argument.
Multipoint opening argument
Consider the commitments $A,B,C,D$ to polynomials $a(X),b(X),c(X),d(X)$. Let's say that $a$ and $b$ were queried at the point $x$, while $c$ and $d$ were queried at both points $x$ and $ωx$. (Here, $ω$ is the primitive root of unity in the multiplicative subgroup over which we constructed the polynomials).
To open these commitments, we could create a polynomial $Q$ for each point that we queried at (corresponding to each relative rotation used in the circuit). But this would not be efficient in the circuit; for example, $c(X)$ would appear in multiple polynomials.
Instead, we can group the commitments by the sets of points at which they were queried: $ {x}AB {x,ωx}CD $
For each of these groups, we combine them into a polynomial set, and create a single $Q$ for that set, which we open at each rotation.
Optimization steps
The multipoint opening optimization takes as input:
 A random $x$ sampled by the verifier, at which we evaluate $a(X),b(X),c(X),d(X)$.
 Evaluations of each polynomial at each point of interest, provided by the prover: $a(x),b(x),c(x),d(x),c(ωx),d(ωx)$
These are the outputs of the vanishing argument.
The multipoint opening optimization proceeds as such:

Sample random $x_{1}$, to keep $a,b,c,d$ linearly independent.

Accumulate polynomials and their corresponding evaluations according to the point set at which they were queried:
q_polys
: $q_{1}(X)q_{2}(X) == a(X)c(X) ++ x_{1}b(X)x_{1}d(X) $q_eval_sets
:[ [a(x) + x_1 b(x)], [ c(x) + x_1 d(x), c(\omega x) + x_1 d(\omega x) ] ]
NB:
q_eval_sets
is a vector of sets of evaluations, where the outer vector goes over the point sets, and the inner vector goes over the points in each set. 
Interpolate each set of values in
q_eval_sets
:r_polys
: $r_{1}(X)s.t.r_{2}(X)s.t. r_{1}(x)r_{2}(x)r_{2}(ωx) === a(x)+x_{1}b(x)c(x)+x_{1}d(x)c(ωx)+x_{1}d(ωx) $ 
Construct
f_polys
which check the correctness ofq_polys
:f_polys
$f_{1}(X)f_{2}(X) == X−xq_{1}(X)−r_{1}(X) (X−x)(X−ωx)q_{2}(X)−r_{2}(X) $If $q_{1}(x)=r_{1}(x)$, then $f_{1}(X)$ should be a polynomial. If $q_{2}(x)=r_{2}(x)$ and $q_{2}(ωx)=r_{2}(ωx)$ then $f_{2}(X)$ should be a polynomial.

Sample random $x_{2}$ to keep the
f_polys
linearly independent. 
Construct $f(X)=f_{1}(X)+x_{2}f_{2}(X)$.

Sample random $x_{3}$, at which we evaluate $f(X)$: $f(x_{3}) == f_{1}(x_{3})x_{3}−xq_{1}(x_{3})−r_{1}(x_{3}) ++ x_{2}f_{2}(x_{3})x_{2}(x_{3}−x)(x_{3}−ωx)q_{2}(x_{3})−r_{2}(x_{3}) $

Sample random $x_{4}$ to keep $f(X)$ and
q_polys
linearly independent. 
Construct
final_poly
, $final_poly(X)=f(X)+x_{4}q_{1}(X)+x_{4}q_{2}(X),$ which is the polynomial we commit to in the inner product argument.
Inner product argument
Halo 2 uses a polynomial commitment scheme for which we can create polynomial commitment opening proofs, based around the Inner Product Argument.
TODO: Explain Halo 2's variant of the IPA.
It is very similar to $PC_{DL}.Open$ from Appendix A.2 of BCMS20. See this comparison for details.
Comparison to other work
BCMS20 Appendix A.2
Appendix A.2 of BCMS20 describes a polynomial commitment scheme that is similar to the one described in BGH19 (BCMS20 being a generalization of the original Halo paper). Halo 2 builds on both of these works, and thus itself uses a polynomial commitment scheme that is very similar to the one in BCMS20.
The following table provides a mapping between the variable names in BCMS20, and the equivalent objects in Halo 2 (which builds on the nomenclature from the Halo paper):
BCMS20  Halo 2 

$S$  $H$ 
$H$  $U$ 
$C$  msm or $P$ 
$α$  $ι$ 
$ξ_{0}$  $z$ 
$ξ_{i}$  challenge_i 
$H_{′}$  $[z]U$ 
$pˉ $  s_poly 
$ωˉ$  s_poly_blind 
$Cˉ$  s_poly_commitment 
$h(X)$  $g(X)$ 
$ω_{′}$  blind / $ξ$ 
$c$  $a$ 
$c$  $a=a_{0}$ 
$v_{′}$  $ab$ 
Halo 2's polynomial commitment scheme differs from Appendix A.2 of BCMS20 in two ways:

Step 8 of the $Open$ algorithm computes a "nonhiding" commitment $C_{′}$ prior to the inner product argument, which opens to the same value as $C$ but is a commitment to a randomlydrawn polynomial. The remainder of the protocol involves no blinding. By contrast, in Halo 2 we blind every single commitment that we make (even for instance and fixed polynomials, though using a blinding factor of 1 for the fixed polynomials); this makes the protocol simpler to reason about. As a consequence of this, the verifier needs to handle the cumulative blinding factor at the end of the protocol, and so there is no need to derive an equivalent to $C_{′}$ at the start of the protocol.
 $C_{′}$ is also an input to the random oracle for $ξ_{0}$; in Halo 2 we utilize a transcript that has already committed to the equivalent components of $C_{′}$ prior to sampling $z$.

The $PC_{DL}.SuccinctCheck$ subroutine (Figure 2 of BCMS20) computes the initial group element $C_{0}$ by adding $[v]H_{′}=[vϵ]H$, which requires two scalar multiplications. Instead, we subtract $[v]G_{0}$ from the original commitment $P$, so that we're effectively opening the polynomial at the point to the value zero. The computation $[v]G_{0}$ is more efficient in the context of recursion because $G_{0}$ is a fixed base (so we can use lookup tables).
Protocol Description
Preliminaries
We take $λ$ as our security parameter, and unless explicitly noted all algorithms and adversaries are probabilistic (interactive) Turing machines that run in polynomial time in this security parameter. We use $negl(λ)$ to denote a function that is negligible in $λ$.
Cryptographic Groups
We let $G$ denote a cyclic group of prime order $p$. The identity of a group is written as $O$. We refer to the scalars of elements in $G$ as elements in a scalar field $F$ of size $p$. Group elements are written in capital letters while scalars are written in lowercase or Greek letters. Vectors of scalars or group elements are written in boldface, i.e. $a∈F_{n}$ and $G∈G_{n}$. Group operations are written additively and the multiplication of a group element $G$ by a scalar $a$ is written $[a]G$.
We will often use the notation $⟨a,b⟩$ to describe the inner product of two likelength vectors of scalars $a,b∈F_{n}$. We also use this notation to represent the linear combination of group elements such as $⟨a,G⟩$ with $a∈F_{n},G∈G_{n}$, computed in practice by a multiscalar multiplication.
We use $0_{n}$ to describe a vector of length $n$ that contains only zeroes in $F$.
Discrete Log Relation Problem. The advantage metric $Adv_{G,n}(A,λ)=Pr[G_{G,n}(A,λ)]$ is defined with respect the following game. $ GameG_{G,n}(A,λ): G←G_{λ}a←A(G)Return(⟨a,G⟩=O∧a=0_{n}) $
Given an $n$length vector $G∈G_{n}$ of group elements, the discrete log relation problem asks for $g∈F_{n}$ such that $g=0_{n}$ and yet $⟨g,G⟩=O$, which we refer to as a nontrivial discrete log relation. The hardness of this problem is tightly implied by the hardness of the discrete log problem in the group as shown in Lemma 3 of [JT20]. Formally, we use the game $G_{G,n}$ defined above to capture this problem.
Interactive Proofs
Interactive proofs are a triple of algorithms $IP=(Setup,P,V)$. The algorithm $Setup(1_{λ})$ produces as its output some public parameters commonly refered to by $pp$. The prover $P$ and verifier $V$ are interactive machines (with access to $pp$) and we denote by $⟨P(x),V(y)⟩$ an algorithm that executes a twoparty protocol between them on inputs $x,y$. The output of this protocol, a transcript of their interaction, contains all of the messages sent between $P$ and $V$. At the end of the protocol, the verifier outputs a decision bit.
Zero knowledge Arguments of Knowledge
Proofs of knowledge are interactive proofs where the prover aims to convince the verifier that they know a witness $w$ such that $(x,w)∈R$ for a statement $x$ and polynomialtime decidable relation $R$. We will work with arguments of knowledge which assume computationallybounded provers.
We will analyze arguments of knowledge through the lens of four security notions.
 Completeness: If the prover possesses a valid witness, can they always convince the verifier? It is useful to understand this property as it can have implications for the other security notions.
 Soundness: Can a cheating prover falsely convince the verifier of the correctness of a statement that is not actually correct? We refer to the probability that a cheating prover can falsely convince the verifier as the soundness error.
 Knowledge soundness: When the verifier is convinced the statement is correct, does the prover actually possess ("know") a valid witness? We refer to the probability that a cheating prover falsely convinces the verifier of this knowledge as the knowledge error.
 Zero knowledge: Does the prover learn anything besides that which can be inferred from the correctness of the statement and the prover's knowledge of a valid witness?
First, we will visit the simple definition of completeness.
Perfect Completeness. An interactive argument $(Setup,P,V)$ has perfect completeness if for all polynomialtime decidable relations $R$ and for all nonuniform polynomialtime adversaries $A$ $Pr[(x,w)∈/R∨⟨P(pp,x,w),V(pp,x)⟩accepts∣∣ pp←Setup(1_{λ})(x,w)←A(pp) ]=1$
Soundness
Complicating our analysis is that although our protocol is described as an interactive argument, it is realized in practice as a noninteractive argument through the use of the FiatShamir transformation.
Public coin. We say that an interactive argument is public coin when all of the messages sent by the verifier are each sampled with fresh randomness.
FiatShamir transformation. In this transformation an interactive, public coin argument can be made noninteractive in the random oracle model by replacing the verifier algorithm with a cryptographically strong hash function that produces sufficiently random looking output.
This transformation means that in the concrete protocol a cheating prover can easily "rewind" the verifier by forking the transcript and sending new messages to the verifier. Studying the concrete security of our construction after applying this transformation is important. Fortunately, we are able to follow a framework of analysis by Ghoshal and Tessaro ([GT20]) that has been applied to constructions similar to ours.
We will study our protocol through the notion of staterestoration soundness. In this model the (cheating) prover is allowed to rewind the verifier to any previous state it was in. The prover wins if they are able to produce an accepting transcript.
StateRestoration Soundness. Let $IP$ be an interactive argument with $r>=r(λ)$ verifier challenges and let the $i$th challenge be sampled from $Ch_{i}$. The advantage metric $Adv_{IP}(P,λ)=Pr[SRS_{P}(λ)]$ of a state restoration prover $P$ is defined with respect to the following game. $ GameSRS_{IP}(λ): win←false;tr←ϵpp←IP.Setup(1_{λ})(x,st_{P})←P_{λ}(pp)RunP_{λ}(st_{P})Return win OracleO_{SRS}(τ=(a_{1},c_{1},...,a_{i−1},c_{i−1}),a_{i}): Ifτ∈trthenIfi≤rthenc_{i}←Ch_{i};tr←tr∣∣(τ,a_{i},c_{i});Returnc_{i}Else ifi=r+1thend←IP.V(pp,x,(τ,a_{i}));tr←(τ,a_{i})Ifd=1then win←trueReturndReturn⊥ $
As shown in [GT20] (Theorem 1) state restoration soundness is tightly related to soundness after applying the FiatShamir transformation.
Knowledge Soundness
We will show that our protocol satisfies a strengthened notion of knowledge soundness known as witness extended emulation. Informally, this notion states that for any successful prover algorithm there exists an efficient emulator that can extract a witness from it by rewinding it and supplying it with fresh randomness.
However, we must slightly adjust our definition of witness extended emulation to account for the fact that our provers are state restoration provers and can rewind the verifier. Further, to avoid the need for rewinding the state restoration prover during witness extraction we study our protocol in the algebraic group model.
Algebraic Group Model (AGM). An adversary $P_{alg}$ is said to be algebraic if whenever it outputs a group element $X$ it also outputs a representation $x∈F_{n}$ such that $⟨x,G⟩=X$ where $G∈G_{n}$ is the vector of group elements that $P_{alg}$ has seen so far. Notationally, we write $[X]$ to describe a group element $X$ enhanced with this representation. We also write $[X]_{i}$ to identify the component of the representation of $X$ that corresponds with $G_{i}$. In other words, $X=i=0∑n−1 [[X]_{i}]G_{i}$
The algebraic group model allows us to perform socalled "online" extraction for some protocols: the extractor can obtain the witness from the representations themselves for a single (accepting) transcript.
State Restoration Witness Extended Emulation Let $IP$ be an interactive argument for relation $R$ with $r=r(λ)$ challenges. We define for all nonuniform algebraic provers $P_{alg}$, extractors $E$, and computationally unbounded distinguishers $D$ the advantage metric $Adv_{IP,R}(P_{alg},D,E,λ)=Pr[WEEreal_{IP,R}(λ)]−Pr[WEEideal_{IP,R}(λ)]$ is defined with the respect to the following games. $ GameWEEreal_{IP,R}(λ): tr←ϵpp←IP.Setup(1_{λ})(x,st_{P})←P_{alg}(pp)RunP_{alg}_{O_{real}}(st_{P})b←D(tr)Returnb=1GameWEEideal_{IP,R}(λ): tr←ϵpp←IP.Setup(1_{λ})(x,st_{P})←P_{alg}(pp)st_{E}←(1_{λ},pp,x)RunP_{alg}_{O_{ideal}}(st_{P})w←E(st_{E},⊥)b←D(tr)Return(b=1)∧(Acc(tr)⟹(x,w)∈R) OracleO_{real}(τ=(a_{1},c_{1},...,a_{i−1},c_{i−1}),a_{i}): Ifτ∈trthenIfi≤rthenc_{i}←Ch_{i};tr←tr∣∣(τ,a_{i},c_{i});Returnc_{i}Else ifi=r+1thend←IP.V(pp,x,(τ,a_{i}));tr←(τ,a_{i})Ifd=1then win←trueReturndReturn⊥OracleO_{ideal}(τ,a): Ifτ∈trthen(r,st_{E})←E(st_{E},[(τ,a)])tr←tr∣∣(τ,a,r)ReturnrReturn⊥ $
Zero Knowledge
We say that an argument of knowledge is zero knowledge if the verifier also does not learn anything from their interaction besides that which can be learned from the existence of a valid $w$. More formally,
Perfect Special HonestVerifier Zero Knowledge. A public coin interactive argument $(Setup,P,V)$ has perfect special honestverifier zero knowledge (PSHVZK) if for all polynomialtime decidable relations $R$ and for all $(x,w)∈R$ and for all nonuniform polynomialtime adversaries $A_{1},A_{2}$ there exists a probabilistic polynomialtime simulator $S$ such that $= Pr⎣⎡ A_{1}(σ,x,tr)=1∣∣ pp←Setup(1_{λ});(x,w,ρ)←A_{2}(pp);tr←⟨P(pp,x,w),V(pp,x,ρ)⟩ ⎦⎤ Pr⎣⎡ A_{1}(σ,x,tr)=1∣∣ pp←Setup(1_{λ});(x,w,ρ)←A_{2}(pp);tr←S(pp,x,ρ) ⎦⎤ $ where $ρ$ is the internal randomness of the verifier.
In this (common) definition of zeroknowledge the verifier is expected to act "honestly" and send challenges that correspond only with their internal randomness; they cannot adaptively respond to the prover based on the prover's messages. We use a strengthened form of this definition that forces the simulator to output a transcript with the same (adversarially provided) challenges that the verifier algorithm sends to the prover.
Protocol
Let $ω∈F$ be a $n=2_{k}$ primitive root of unity forming the domain $D=(ω_{0},ω_{1},...,ω_{n−1})$ with $t(X)=X_{n}−1$ the vanishing polynomial over this domain. Let $k,n_{g},n_{a}$ be positive integers. We present an interactive argument $Halo=(Setup,P,V)$ for the relation $R=⎩⎨⎧ ((g(X,C_{0},C_{1},...,C_{n_{a}−1}));(a_{0}(X),a_{1}(X,C_{0}),...,a_{n_{a}−1}(X,C_{0},C_{1},...,C_{n_{a}−1})) ):g(ω_{i},C_{0},C_{1},...,C_{n_{a}−1})=0∀i∈[0,2_{k}) ⎭⎬⎫ $ where $a_{0},a_{1},...,a_{n_{a}−1}$ are (multivariate) polynomials with degree $n−1$ in $X$ and $g$ has degree $n_{g}(n−1)$ in $X$.
$Setup(λ)$ returns $pp=(G,F,G∈G_{n},U,W∈G)$.
For all $i∈[0,n_{a})$:
 Let $p_{i}$ be the exhaustive set of integers $j$ (modulo $n$) such that $a_{i}(ω_{j}X,⋯)$ appears as a term in $g(X,⋯)$.
 Let $q$ be a list of distinct sets of integers containing $p_{i}$ and the set $q_{0}={0}$.
 Let $σ(i)=q_{j}$ when $q_{j}=p_{i}$.
Let $n_{q}$ denote the size of $q$, and let $n_{e}$ denote the size of every $p_{i}$ without loss of generality.
In the following protocol, we take it for granted that each polynomial $a_{i}(X,⋯)$ is defined such that $n_{e}+1$ blinding factors are freshly sampled by the prover and are each present as an evaluation of $a_{i}(X,⋯)$ over the domain $D$.
 $P$ and $V$ proceed in the following $n_{a}$ rounds of interaction, where in round $j$ (starting at $0$)
 $P$ sets $a_{j}(X)=a_{j}(X,c_{0},c_{1},...,c_{j−1})$
 $P$ sends a hiding commitment $A_{j}=⟨a_{′},G⟩+[⋅]W$ where $a_{′}$ are the coefficients of the univariate polynomial $a_{j}(X)$ and $⋅$ is some random, independently sampled blinding factor elided for exposition.
 $V$ responds with a challenge $c_{j}$.
 $P$ and $V$ set $g_{′}(X)=g(X,c_{0},c_{1},...,c_{n_{a}−1})$.
 $P$ sends a commitment $R=⟨r,G⟩+[⋅]W$ where $r∈F_{n}$ are the coefficients of a randomly sampled univariate polynomial $r(X)$ of degree $n−1$.
 $P$ computes univariate polynomial $h(X)=t(X)g_{′}(X) $ of degree $n_{g}(n−1)−n$.
 $P$ computes at most $n−1$ degree polynomials $h_{0}(X),h_{1}(X),...,h_{n_{g}−2}(X)$ such that $h(X)=i=0∑n_{g}−2 X_{ni}h_{i}(X)$.
 $P$ sends commitments $H_{i}=⟨h_{i},G⟩+[⋅]W$ for all $i$ where $h_{i}$ denotes the vector of coefficients for $h_{i}(X)$.
 $V$ responds with challenge $x$ and computes $H_{′}=i=0∑n_{g}−2 [x_{ni}]H_{i}$.
 $P$ sets $h_{′}(X)=i=0∑n_{g}−2 x_{ni}h_{i}(X)$.
 $P$ sends $r=r(x)$ and for all $i∈[0,n_{a})$ sends $a_{i}$ such that $(a_{i})_{j}=a_{i}(ω_{(p_{i})_{j}}x)$ for all $j∈[0,n_{e}]$.
 For all $i∈[0,n_{a})$ $P$ and $V$ set $s_{i}(X)$ to be the lowest degree univariate polynomial defined such that $s_{i}(ω_{(p_{i})_{j}}x)=(a_{i})_{j}$ for all $j∈[0,n_{e})$.
 $V$ responds with challenges $x_{1},x_{2}$ and initializes $Q_{0},Q_{1},...,Q$