Costs and Alternatives

Arithmetic circuits have a different cost model than software. In the software cost model, software executes machine instructions, but in the circuit cost model, relations are certified by constraints. Unfortunately, while Decaf is a clearly superior choice in the software context, in the circuit context it imposes some additional costs, which must be weighed against its benefits.

At a high level, Decaf implements a prime-order group using a non-prime-order curve by constructing a group quotient. Internally, group elements are represented by curve points, with a custom equality check so that equivalent representatives are considered equal, an encoding function that encodes equivalent representatives as identical bitstrings, and a decoding function that only accepts canonical encodings of valid representatives.

To do this, the construction defines a canonical encoding on a Jacobi quartic curve mod its 2-torsion (a subgroup of size 4) by making two independent sign choices. Then, it uses an isogeny to transport this encoding from the Jacobi quartic to a target curve that will be used to actually implement the group operations. This target curve can be an Edwards curve or a Montgomery curve. The isogenies are only used for deriving the construction. In implementations, all of these steps are collapsed into a single set of formulas that perform encoding and decoding on the target curve.

In other words, one way to think about the Decaf construction is as some machinery that transforms two sign choices into selection of a canonical representative. Ristretto adds extra machinery to handle cofactor 8 by making an additional sign choice.

In the software cost model, where software executes machine instructions, this construction is essentially free, because the cost of both the Decaf and conventional Edwards encodings are dominated by the cost of computing an inverse or an inverse square root, and the cost of the sign checks is insignificant.

However, in the circuit cost model, where relations are certified by various constraints, this is no longer the case. On the one hand, certifying a square root or an inverse just requires checking that or that , which is much cheaper than actually computing or . On the other hand, performing a sign check involves bit-constraining a field element, requiring hundreds of constraints.

Sign checks

The definition of which finite field elements are considered nonnegative is essentially arbitrary. The Decaf paper suggests three possibilities:

  • using the least significant bit, defining to be nonnegative if the least absolute residue for is even;

  • using the most significant bit, defining to be nonnegative if the least absolute residue for is in the range ;

  • for fields where , using the Legendre symbol, which distinguishes between square and nonsquare elements.

Using the Legendre symbol is very appealing in the circuit context, since it has an algebraic definition and, at least in the case of square elements, very efficient certification. For instance, if square elements are chosen to be nonnegative, then certifying that is nonnegative requires only one constraint, . However, the reason for the restriction to fields is that and should have different signs, which can only be the case if is nonsquare. Unfortunately, many SNARK-friendly curves, including BLS12-377, are specifically chosen so that for as large a power as possible (e.g., in the case of BLS12-377).

This leaves us with either the LSB or MSB choices. The least significant bit is potentially simpler for implementations, since it is actually the low bit of the encoding of , while the most significant bit isn’t, because it measures from , not a bit position , so it seems to require a comparison or range check to evaluate. However, these choices are basically equivalent, in the following sense:

Lemma.1

The most significant bit of is if and only if the least significant bit of is .

Proof.

The MSB of is if and only if , but this means that , which is even, is the least absolute residue, so the LSB of is also . On the other hand, the MSB of is if and only if , i.e., if , i.e., if . This means that the least absolute residue of is ; since is even and is odd, this is odd and has LSB .

This means that transforming an LSB check to an MSB check or vice versa requires multiplication by or , which costs at most one constraint.

Checking the MSB requires checking whether a value is in the range . Using Daira Hopwood’s optimized range constraints, the range check costs 2. However, the input to the range check is a bit-constrained unpacking of a field element, not a field element itself. This unpacking costs .

Checking the LSB is no less expensive, because although the check only examines one bit, the circuit must certify that the bit-encoding is canonical. This requires checking that the value is in the range , which also costs , and as before, the unpacking costs .

In other words, checking the sign of a field element costs , or in the case where the field element is already bit-encoded for other reasons. These checks are the dominant cost for encoding and decoding, which both require two sign checks. Decoding from bits costs c. , decoding from a field element costs c. , and encoding costs c. regardless of whether the output is encoded as bits or as a field element.

Alternative approaches to handling cofactors

Decaf constructs a prime-order group whose encoding and decoding methods perform validation. A more conventional alternative approach is to use the underlying elliptic curve directly, restrict to its prime-order subgroup, and do subgroup validation separately from encoding and decoding. If this validation is done correctly, it provides a prime-order group. However, because validation is an additional step, rather than an integrated part of the encoding and decoding methods, this approach is necessarily more brittle, because each implementation must be sure to do both steps.

In the software cost model, there is no reason to use subgroup validation, because it is both more expensive and more brittle than Decaf or Ristretto. However, in the circuit cost model, there are cheaper alternatives, previously analyzed by Daira Hopwood in the context of Ristretto for JubJub (1, 2).

Multiplication by the group order.

The first validation method is to do a scalar multiplication and check that . Because the prime order is fixed, this scalar multiplication can be performed more efficiently using a hardcoded sequence of additions and doublings.

Cofactor preimage.

The second validation method provides a preimage in affine coordinates . Because the image of is the prime-order subgroup, checking that satisfies the curve equation and that checks that is in the prime-order subgroup.

In the software context, computing and computing cost about the same, although both are an order of magnitude more expensive than decoding. But in the circuit context, the prover can compute outside of the circuit and use only a few constraints to check the curve equation and two doublings. These costs round to zero compared to sign checks, so the validation is almost free.

The standard “compressed Edwards y” format encodes a point by the -coordinate and a sign bit indicating whether is nonnegative. In software, the cost of encoding and decoding are about the same, and dominated by taking an inverse square root. In circuits, the costs of encoding and decoding are also about the same, but they are instead dominated by a sign check that matches the sign of the recovered -coordinate with the supplied sign bit. This costs c. as above.

Comparison and discussion

This table considers only approximate costs.

Operationdecaf377Compressed Ed + Preimage
Decode (from bits)400C400C
Decode (from )750C325C
Encode (to bits)750C750C
Encode (to )750C325C

When decoding from or encoding to field elements, the marginal cost of Decaf compared to the compressed Edwards + cofactor preimage is an extra bit-unpacking and range check. While this effectively doubles the number of constraints, the marginal cost of c. is still small relative to other operations like a scalar multiplication, which at 6 constraints per bit is approximately .

When decoding from or encoding to bits, the marginal cost of Decaf disappears. When the input is already bit-constrained, Decaf’s first sign check can reuse the bit constraints, saving c. , but the compressed Edwards encoding must range-check the bits (which Decaf already does), costing c. extra. Similarly, in encoding, Decaf’s second sign check produces bit-constrained variables for free, while the compressed Edwards encoding spends c. bit-constraining and range-checking them.

However, in the software context, the prime-order validation check costs approximately 10x more than the cost of either encoding. Many applications require use of the embedded group both inside and outside of the circuit, and uses outside of the circuit may have additional resource constraints (for instance, a hardware token creating a signature authorizing delegated proving, etc.).

Performing validation as an additional, optional step also poses additional risks. While a specification may require it to be performed, implementations that skip the check will appear to work fine, and the history of invalid-point attacks (where implementations should, but don’t, check that point coordinates satisfy the curve equation) suggests that structuring validation as an integral part of encoding and decoding is a safer design. This may not be a concern for a specific application with a single, high-quality implementation that doesn’t make mistakes, but it’s less desirable for a general-purpose construction.

In summary, Decaf provides a construction that works the same way inside and outside of a circuit and integrates validation with the encoding, imposing only a modest cost for use in circuits and no costs for lightweight, software-only applications, making it a good choice for general-purpose constructions.

1

I have no idea whether this is common knowledge; I learned of this fact from its use in Mike Hamburg’s Ed448-Goldilocks implementation.

2

The value 73 is computed as:

from itertools import groupby

def cost(k):
  return min(k-1, 2)

def constraints(bound):
  costs = [cost(len(list(g))+1) for (c, g) in groupby(bound.bits()[:-1]) if c == 1]
  return sum(costs)

constraints(ZZ((q-1)/2))

as here.