## 1 Introduction

The availability of large-scale quantum computers is getting ever closer to reality, and with it, all of the public-key cryptosystems currently in use, which rely on number theory problems (e.g., factorization) and discrete logarithm problems will become obsolete [41]. Therefore, it is of extreme importance to be able to offer a credible alternative that can resist attackers equipped with quantum technology. In this regard, NIST’s call for proposals for post-quantum standardization is a further reassurance about the need for solid post-quantum proposals. Furthermore, considering the desired life of the encrypted data, and the lengthy timeframe for such a complex standardization process, it is clear how convincing research work in post-quantum cryptography is not only necessary, but also urgent.

Code-based cryptography is one of the main candidates for this task. The area is generally based on the syndrome decoding problem [10], which has shown no vulnerabilities to quantum attacks over the years. Since McEliece’s seminal work [30] in 1978, many variants and modifications have been proposed, trying to balance security and efficiency and in particular dealing with inherent flaws such as the large size of the public keys. In fact, while the original McEliece’s cryptosystem (based on binary Goppa codes) is still formally unbroken, it features a key of several tenths of kilobytes, which has effectively prevented its use in many applications.

There are currently two main trends to deal with this issue, and they both involve structured matrices: the first, is based on “traditional” algebraic codes, and in particular alternant codes such as Goppa or generalized Srivastava codes; the second suggests to use sparse matrices as in LDPC/MDPC codes [3, 32]. This work builds on the former approach, initiated in 2009 by Berger et al. [9], who proposed Quasi-Cyclic (QC) codes, and Misoczki and Barreto [31], suggesting Quasi-Dyadic (QD) codes instead (later generalized to Quasi-Monoidic (QM) codes [8]). Both proposals feature very compact public keys due to the introduction of the extra algebraic structure, but unfortunately this also leads to a vulnerability. Indeed, Faugère et al. [22] devised a clever attack (known simply as FOPT) which exploits the algebraic structure to build a system of equations, which can successively be solved using Gröbner bases techniques. As a result, the QC proposal is heavily compromised, while the QD/QM approach needs to be treated with caution. In fact, for a proper choice of parameters, it is still possible to design secure schemes, using for instance binary Goppa codes, or Generalized Srivastava (GS) codes as suggested by Persichetti in [36].

### Our contribution.

In this paper, we present DAGS^{1}, a key encapsulation mechanism that follows the QD approach using GS codes. KEMs are the primitive favored by NIST for key exchange schemes, and can be used to build encryption schemes, for example using the hybrid encryption paradigm introduced by Cramer and Shoup [18]. To the best of our knowledge, this is the first code-based KEM that uses quasi-dyadic codes. Another NIST submission, named BIG QUAKE [44], proposes a scheme based on quasi-cyclic codes.

Our KEM achieves IND-CCA security, following the recent framework by Kiltz et al. [27], and features compact public keys and efficient encapsulation and decapsulation algorithms. We modulate our parameters to achieve an efficient scheme, while at the same time keeping out of range of the FOPT attack. We provide an initial performance analysis of our scheme as well as access to our reference code; the team is currently working at several additional, optimized implementations, using C++, assembly language, and hardware (FPGA).

### Related work.

We show that our proposal compares well with other post-quantum KEMs. These include the classic McEliece approach [47], as well as more recent proposals such as BIKE [45] and the aforementioned BIG QUAKE.

The “Classic McEliece” project is an evolution of the well-known McBits [12] (based on the work of Persichetti [37]), and benefits from a well-understood security assessment but suffers from the usual public key size issue. BIKE, a protocol based on QC-MDPC codes, is the result of a merge between two independently published works with similar background, namely CAKE [7] and Ouroboros [19]. The scheme possesses some very nice features like compact keys and an easy implementation approach, but has currently some potential drawbacks. In fact, the QC-MDPC encryption scheme on which it is based is susceptible to a reaction attack by Guo, Johansson and Stankovski (GJS) [25], and thus the protocol is forced to employ ephemeral keys. Moreover, due to its non-trivial Decoding Failure Rate (DFR), achieving IND-CCA security becomes very hard, so that the BIKE protocol only claims to be IND-CPA secure.

Finally, BIG QUAKE continues the line of work of [9] and proposes to use quasi-cyclic Goppa codes. Due to the particular nature of the FOPT attack and its successors [21], it seems harder to provide security with this approach, and the protocol chooses very large parameters in order to do so. We will discuss attack and parameters in Section 5.

More distantly related are lattice-based schemes like NewHope [2] and Frodo [14], based respectively on LWE and its ring variant. While these schemes are not necessarily a direct comparison term, it is nice to observe that DAGS offers comparable performance.

### Organization of the paper.

The paper is organized as follows. We start by giving some preliminary notions in Section 2. We describe the DAGS protocol in Section 3, and we discuss its provable security in Section 4, showing that DAGS is IND-CCA secure in the random oracle model as well as the quantum random oracle model. Section 5 features a discussion about practical security and known attacks, which include general decoding attacks (information set decoding and the like) as well as algebraic attacks; we then present parameters for the scheme. Performance details are given in Section 6. Finally, we conclude in Section 7.

## 2 Preliminaries

### 2.1 Notation

We will use the following conventions throughout the rest of the paper:

*a*a constant a vector$\bm{a}$ *A*a matrix an algorithm or (hash) function$\mathcal{A}$ a set$\U0001d5a0$ the concatenation of vectors$(\bm{a}\parallel \bm{b})$ and$\bm{a}$ $\bm{b}$ the diagonal matrix formed by the vector$\mathrm{Diag}\left(\bm{a}\right)$ $\bm{a}$ the${I}_{n}$ identity matrix$n\times n$ choosing a random element from a set or distribution$\stackrel{\text{\$}}{\leftarrow}$ the length of a shared symmetric key$\ell $

### 2.2 Linear codes

We briefly recall some fundamental notions from coding theory. The *Hamming weight* of a vector

An *-linear code**n* and dimension *k* over *k*-dimensional vector subspace of

A linear code can be represented by means of a matrix *generator matrix*, whose rows form a basis for the vector space defining the code. Alternatively, a linear code can also be represented as kernel of a matrix *parity-check matrix*, i.e. *syndrome* of a vector

### 2.3 Structured matrices and GS codes

Given a ring *dyadic* matrix *signature*. Moreover, *r* rows. Finally, we call a matrix *quasi-dyadic* if it is a block matrix whose component blocks are

If *n* is a power of 2, then every

where each block is a

For *q*, let *generalized Srivastava code* of order *st* and length *n* is defined by a parity-check matrix of the form

where each block is given by

The parameters for such a code are the length

## 3 Construction

The core idea of DAGS is to use GS codes which are defined by matrices in quasi-dyadic form. In particular, the public key of the scheme is the generator matrix of such a code, which, being quasi-dyadic, can be described using just the signature of each block. This allows to obtain a very compact public key. Now, it can be easily proved that every GS code with

Misoczki and Barreto showed in [31, Theorem 2] that the intersection of the set of Cauchy matrices with the set of dyadic matrices is not empty if the code is defined over a field of characteristic 2, and the dyadic signature

On the other hand, it is evident from Definition 2.3 that if we permute the rows of *H* to constitute

we obtain an equivalent parity-check matrix for a GS code, given by

The key generation process exploits first of all the fundamental equation to build a Cauchy matrix. The matrix is then successively powered (element by element) forming several blocks which are superimposed and then multiplied by a random diagonal matrix. Thanks to the observation above, we have now formed the matrix

### 3.1 Algorithms

We are now ready to introduce the three algorithms that form DAGS. System parameters are the code length *n* and dimension *k*, the values *s* and *t* which define a GS code, the cardinality of the base field *q* and the degree of the field extension *m*. In addition, we have

DAGS is a key encapsulation mechanism and as such it is composed of three algorithms – Key Generation, Encapsulation and Decapsulation – which will present below in the respective order.

- (1)Generate the dyadic signature
:$\bm{h}$ - (1)(a)Choose a random non-zero distinct
and${h}_{0}$ for${h}_{j}$ .$j={2}^{l},l=0,\dots ,\lfloor \mathrm{log}{q}^{m}\rfloor $ - (1)(b)Form the remaining elements using (3.1).
- (1)(c)Return a selection
^{2}of blocks of dimension*s*up to length*n*.

- (1)(a)Choose a random non-zero distinct
- (2)Build the Cauchy support:
- (2)(a)Choose a random
^{3}offset .$\omega \stackrel{\text{\$}}{\leftarrow}{\mathbb{F}}_{{q}^{m}}$ - (2)(b)Compute
for${u}_{i}=\frac{1}{{h}_{i}}+\omega $ .$i=0,\dots ,s-1$ - (2)(c)Compute
for${v}_{j}=\frac{1}{{h}_{j}}+\frac{1}{{h}_{0}}+\omega $ .$j=0,\dots ,n-1$ - (2)(d)Set
and$\bm{u}=({u}_{0},\dots ,{u}_{s-1})$ .$\bm{v}=({v}_{0},\dots ,{v}_{n-1})$

- (2)(a)Choose a random
- (3)Form the Cauchy matrix
.${\hat{H}}_{1}=C(\bm{u},\bm{v})$ - (4)Build
,${\hat{H}}_{i}$ , by raising each element of$i=2,\dots ,t$ to the power of${\hat{H}}_{1}$ *i*. - (5)Superimpose the blocks
in ascending order to form matrix${\hat{H}}_{i}$ .$\hat{H}$ - (6)Generate the vector
by sampling uniformly at random elements in$\bm{z}$ with the restriction${\mathbb{F}}_{{q}^{m}}$ for${z}_{is+j}={z}_{is}$ ,$i=0,\dots ,{n}_{0}-1$ .$j=0,\dots ,s-1$ - (7)Set
${y}_{j}=\frac{{z}_{j}}{{\prod}_{i=0}^{s-1}{\left({u}_{i}-{v}_{j}\right)}^{t}}\hspace{1em}\text{for}j=0,\dots ,n-1\text{and}\bm{y}=({y}_{0},\dots ,{y}_{n-1}).$ - (8)Form
.$H=\hat{H}\cdot \mathrm{Diag}\left(\bm{z}\right)$ - (9)Project
*H*onto using the co-trace function; call this${\mathbb{F}}_{q}$ .${H}_{\text{base}}$ - (10)Write
in systematic form${H}_{\text{base}}$ .$(M\mid {I}_{n-k})$ - (11)The public key is the generator matrix
.$G=({I}_{k}\mid {M}^{T})$ - (12)The private key is the pair
.$(\bm{v},\bm{y})$

The encapsulation and decapsulation algorithms follow the paradigm of [27] to obtain an IND-CCA secure KEM from a PKE, and as such, they make use of two functions

- (1)Choose
.$\bm{m}\stackrel{\text{\$}}{\leftarrow}{\mathbb{F}}_{q}^{{k}^{\prime}}$ - (2)Compute
and$\bm{r}=\mathcal{G}\left(\bm{m}\right)$ .$\bm{d}=\mathscr{H}\left(\bm{m}\right)$ - (3)Parse
as$\bm{r}$ then set$(\bm{\rho}\parallel \bm{\sigma})$ .$\bm{\mu}=(\bm{\rho}\parallel \bm{m})$ - (4)Generate the error vector
of length$\bm{e}$ *n*and weight*w*from .$\bm{\sigma}$ - (5)Compute
.$\bm{c}=\bm{\mu}G+\bm{e}$ - (6)Compute
.$\bm{k}=\mathcal{K}\left(\bm{m}\right)$ - (7)Output the ciphertext
; the encapsulated key is$(\bm{c},\bm{d})$ .$\bm{k}$

The decapsulation algorithm consists mainly of decoding the noisy codeword received as part of the ciphertext. This is done using the alternant decoding algorithm described in [29, Chapter 12, Section 9] and requires the parity-check matrix to be in alternant form.

- (1)Get the parity-check matrix
in alternant form from a private key${H}^{\prime}$ ^{4}. - (2)Use
to decode${H}^{\prime}$ and obtain the codeword$\bm{c}$ and the error${\bm{\mu}}^{\prime}G$ .${\bm{e}}^{\prime}$ - (3)Output
if decoding fails or$\perp $ .$\mathrm{wt}\left({\bm{e}}^{\prime}\right)\ne w$ - (4)Recover
and parse it as${\bm{\mu}}^{\prime}$ .$({\bm{\rho}}^{\prime}\parallel {\bm{m}}^{\prime})$ - (5)Compute
and${\bm{r}}^{\prime}=\mathcal{G}\left({\bm{m}}^{\prime}\right)$ .${\bm{d}}^{\prime}=\mathscr{H}\left({\bm{m}}^{\prime}\right)$ - (6)Parse
as${\bm{r}}^{\prime}$ .$({\bm{\rho}}^{\prime \prime}\parallel {\bm{\sigma}}^{\prime})$ - (7)Generate the error vector
of length${\bm{e}}^{\prime \prime}$ *n*and weight*w*from .${\bm{\sigma}}^{\prime}$ - (8)If
, output${\bm{e}}^{\prime}\ne {\bm{e}}^{\prime \prime}\vee {\bm{\rho}}^{\prime}\ne {\bm{\rho}}^{\prime \prime}\vee \bm{d}\ne {\bm{d}}^{\prime}$ .$\perp $ - (9)Else, compute
.$\bm{k}=\mathcal{K}\left({\bm{m}}^{\prime}\right)$ - (10)The decapsulated key is
.$\bm{k}$

DAGS is built upon the McEliece cryptosystem, with a notable exception. In fact, we incorporate the “randomized” version of McEliece by Nojima et al. [35] into our scheme. This is extremely beneficial for two distinct aspects: first of all, it allows us to use a much shorter vector

The selection of the parameters for the scheme will be discussed in Section 5.4.

## 4 KEM security

In this section, we discuss some aspects of provable security, and in particular we show that DAGS satisfies the notion of IND-CCA security for KEMs, as defined below.

The adaptive chosen-ciphertext attack game for a KEM proceeds as follows:

- (1)Query a key generation oracle to obtain a public key pk.
- (2)Make a sequence of calls to a decryption oracle, submitting any string
of the proper length. The oracle will respond with$\bm{c}$ .$\mathrm{Decaps}(\text{\mathit{s}\mathit{k}},\bm{c})$ - (3)Query an encryption oracle. The oracle runs
to generate a pair$\mathrm{Encaps}\left(\text{\mathit{p}\mathit{k}}\right)$ , then chooses a random$(\stackrel{~}{\bm{k}},\stackrel{~}{\bm{c}})$ and replies with the “challenge” ciphertext$b\in \{0,1\}$ where$({\bm{k}}^{*},\stackrel{~}{\bm{c}})$ if${\bm{k}}^{*}=\stackrel{~}{\bm{k}}$ or$b=1$ is a random string of length${\bm{k}}^{*}$ otherwise.$\ell $ - (4)Keep performing decryption queries. If the submitted ciphertext is
, the oracle will return${\bm{c}}^{*}$ .$\perp $ - (5)Output
.${b}^{*}\in \{0,1\}$

The adversary succeeds if *advantage* of

We say that a KEM is secure if the advantage

Before discussing the IND-CCA security of DAGS, we show that the underlying PKE (i.e. randomized McEliece, see [35]) satisfies a simple property. This will allow us to get better security bounds in our reduction.

Consider a probabilistic PKE with randomness set *spread* if for a given key pair *m* and an element *y* in the ciphertext domain, we have

for a certain

The definition above is presented as in [27], but note that in fact this corresponds to the notion of γ-*uniformity* given by Fujisaki and Okamoto in [24], except for a change of constants. In other words, a scheme is γ-spread if it is

It was proved in [16] that a simple variant of the (classic) McEliece PKE is γ-uniform for *k* is the code dimension as usual (more in general,

*Randomized McEliece is γ-uniform for *

Let *w* from the code, or it is not. If it is not, the probability of *w* from the code; then there is only one choice of *w* is below the GV bound), i.e. the probability of

We are now ready to present the security results.

*Let *

^{5}and

The thesis is a consequence of the results presented in [27, Section 3.3]. In fact, our scheme follows the

The value *d* included in the KEM ciphertext does not contribute to the security result above, but it is a crucial factor to provide security in the Quantum Random Oracle Model (QROM). We present this in the next theorem.

*Let *

^{6}and

The thesis is a consequence of the results presented in Section 4.4 of [27]. In fact, our scheme follows the

## 5 Practical security and parameters

Having proved that DAGS satisfies the notion of IND-CCA security for KEMs, we now move onto a treatment of practical security issues. In particular, we will briefly present the hard problem on which DAGS is based, and then discuss the main attacks on the scheme and related security concerns.

### 5.1 Hard problems from coding theory

Most of the code-based cryptographic constructions are based on the hardness of the following problem, known as the (*q*-ary) *Syndrome Decoding Problem (SDP)*.

*Given an $\left(n-k\right)\times n$ full-rank matrix H over ${F}_{q}$, a vector $\mathbf{s}\in {F}_{q}^{n-k}$, and a non-negative integer w, find a vector $\mathbf{e}\in {F}_{q}^{n}$ of weight w such that $H{\mathbf{e}}^{T}=\mathbf{s}$.*

The corresponding decision problem was proved to be NP-complete in 1978 [10], but only for binary codes. In 1994, Barg proved that this result holds for codes over all finite fields ([5], in Russian, and [6, Theorem 4.1]).

In addition, many schemes (including the original McEliece proposal) require the following computational assumption.

*The public matrix output by the key generation algorithm is computationally indistinguishable from a uniformly chosen matrix of the same size.*

The assumption above is historically believed to be true, except for very particular cases. For instance, there exists a distinguisher (Faugère et al. [20]) for cryptographic protocols that make use of high-rate Goppa codes (like the CFS signature scheme [17]). Moreover, it is worth mentioning that the “classical” methods for obtaining an indistinguishable public matrix, such as the use of scrambling matrices *S* and *P*, are rather outdated and unpractical and can introduce vulnerabilities to the scheme as per the work of Strenzke et al. [42, 43]. Thus, traditionally, the safest method (Biswas and Sendrier, [13]) to obtain the public matrix is simply to compute the systematic form of the private matrix.

### 5.2 Decoding attacks

The main approach for solving SDP is the technique known as Information Set Decoding (ISD), first introduced by Prange [39], which targets directly the error vector and aims at decoding without knowing the underlying structure of the code (i.e. treating the code as truly random). Among several variants and generalizations, Peters showed [38] that it is possible to apply Prange’s approach to generic *q*-ary codes. Other approaches such as statistical decoding [1, 33] are usually considered less efficient. Thus, when choosing parameters, we will focus mainly on defeating attacks of the ISD family.

Hamdaoui and Sendrier in [26] provide non-asymptotic complexity estimates for ISD in the binary case. For codes over

#### Quantum speedup.

Bernstein in [11] shows that Grover’s algorithm applies to ISD-like algorithms, effectively halving the asymptotic exponent in the complexity estimates. Later, it was proved in [28] that several variants of ISD have the potential to achieve a better exponent, however the improvement was disappointingly away from the factor of 2 that could be expected. For this reason, we simply treat the best quantum attack on our scheme to be “traditional” ISD (Prange) combined with Grover search.

### 5.3 Algebraic attacks

While, as we discussed above, recovering a private matrix from a public one is in general a very difficult problem, the presence of special algebraic properties and additional structure in the code can have a considerable effect in lowering this difficulty. It turns out that, in the case of alternant codes for instance, there are indeed efficient methods that exploit this issue.

#### Solving systems of equations.

A very effective structural attack was introduced by Faugère et al. in [22]. The attack (for convenience referred to as FOPT) relies on the simple property

The attack was originally aimed at two variants of McEliece, introduced respectively in [9] and [31]. The first variant, using quasi-cyclic codes, was easily broken in all proposed parameters and falls out of the scope of this paper. The second variant, instead, only considered quasi-dyadic Goppa codes. In this case too, most of the parameters proposed have been broken, except for the binary case (i.e. base field *m*. This is because, probably for comparison reasons, all the proposed parameters were chosen so that the value *m* plays a key role in evaluating the complexity of the attack.

#### Attack complexity.

Following up on their own work, the authors in [23] produced a paper which analyzes the attack in detail, with the aim of evaluating its complexity at least somewhat rigorously. At the core of the attack, there is an affine bilinear system, which is derived from the initial system of equations by applying various algebraic relations due to the quasi-dyadic structure. This bilinear system has *X* and *Y* “free” variables (after applying the relations) of an alternant parity-check matrix *H* with *degree of regularity* (i.e. the maximal degree of the polynomials appearing during the computation) is bounded above by

Details of FOPT applied to quasi-dyadic Goppa codes [23].

q | m | n | k | Time (s) / | Operations | ||||

2 | 16 | 3584 | 1536 | 56 | 60 | 15 | N/A | ||

8 | 3584 | 1536 | 56 | 60 | 7 | 1776.3 / | |||

4 | 2048 | 1024 | 32 | 36 | 3 | 0000.5 / | |||

2 | 1280 | 768 | 20 | 24 | 1 | 0000.03 / | |||

2 | 640 | 512 | 10 | 14 | 1 | 0000.03 / | |||

2 | 768 | 512 | 6 | 11 | 1 | 0000.02 / | |||

2 | 1024 | 512 | 4 | 10 | 1 | 0000.11 / | |||

2 | 512 | 256 | 4 | 9 | 1 | 0000.06 / | |||

2 | 640 | 384 | 5 | 10 | 1 | 0000.02 / | |||

2 | 768 | 512 | 6 | 11 | 1 | 0000.01 / | |||

2 | 1280 | 768 | 5 | 11 | 1 | 0000.05 / | |||

2 | 1536 | 1024 | 6 | 12 | 1 | 0000.06 / | |||

4 | 4096 | 3584 | 32 | 37 | 3 | 0007.1 / | |||

2 | 3072 | 2048 | 6 | 13 | 1 | 0000.15 / |

It is possible to observe several facts. In every set of parameters, for instance, *m* grows. In fact, the attack could not be performed in practice on the first set of parameters (hence the N/A).

The first three groups of parameters are taken from the preliminary (unpublished) version of [31, Tables 2, 3 and 5, respectively], while the last group consists of some ad hoc parameters generated by the FOPT authors. It stands out the absence of parameters from [31, Table 4]. In fact, all of these parameters used

Towards the end of [23], the authors present a bound on the theoretical complexity of computing a Gröbner base of the affine bilinear system which is at the core of the attack. They then evaluate this bound and compare it with the number of operations required in practice (last column of Table 1). The bound is given by

where *D* is the degree of regularity of the system, i.e.

and

As it turns out this bound is quite loose, being sometimes above and sometimes below the experimental results, depending on which set of parameters is considered. As such, it is to be read as a grossly approximate indication of the expected complexity of a parameter set, and it only allows to have a rough idea of the security provided for each set. Nevertheless, since are able to compute the bound for all DAGS proposed parameters, we will keep this number in mind when proposing parameters (Section 5.4), to make sure our choices are at least not obviously insecure.

As a bottom-line, it is clear that the complexity of the attack scales somewhat proportionally to the value

Since GS codes are also alternant codes, the attack can be applied to our proposal as well. There is, however, one very important difference to keep in mind. In fact, it is shown in [36] that, thanks to the particular structure of GS codes, the dimension of the solution space is defined by *m* small while still achieving a large dimension for the solution space. We will discuss parameter selection in detail in Section 5.4 as already mentioned.

#### Folded codes.

Recently, an extension of the FOPT attack appeared in [21]. In this work, the authors introduce a new technique called “folding”, and show that it is possible to reduce the complexity of the FOPT attack to the complexity of attacking a smaller code (the “folded” code). This is a consequence of the strong properties of the automorphism group that is present in the alternant codes used. The attack turns out to be very efficient against Goppa codes, as it is possible to recover a folded code which is also a Goppa code. As a consequence, it is possible to tweak the attack to solve a different, augmented system of equations (named *binary* Goppa codes, leading to a third system of equations referred to as

The paper concentrates on attacking several parameters that were proposed for signature schemes and encryption schemes in various follow-up works that build and expand on [9] and [31]. The latter includes, among others, some of the parameters presented in Table 1. It turns out that codes designed to work for signature schemes are very easy to attack (due to their particular nature); however, the situation for encryption is more complex. The authors are able to obtain a speedup in the attack times for previously investigated parameters, but some of the parameters could still not be solved in practice. We report the results in Table 2, where we indicate the type of system chosen to be solved, and we keep some of the previously-shown parameters for ease of comparison.

Details of folding attack applied to quasi-dyadic Goppa codes [23].

q | m | n | k | System | New attack | FOPT | ||

4 | 2048 | 1024 | 32 | 00.01 s | 0000.5 s | |||

4 | 4096 | 3584 | 32 | 00.01 s | 0007.1 s | |||

8 | 3584 | 1536 | 56 | 00.04 s | 1776.3 s | |||

2 | 16 | 4864 | 4352 | 152 | 18 s | N/A | ||

2 | 12 | 3200 | 1664 | 25 | N/A | |||

2 | 14 | 5376 | 3584 | 42 | N/A | |||

2 | 15 | 11264 | 3584 | 22 | N/A | |||

2 | 16 | 6912 | 2816 | 27 | N/A | |||

2 | 16 | 8192 | 4096 | 32 | N/A |

The authors do not report timings for codes that were already broken with FOPT in negligible time (which is the case for all the parameter sets where *q* not a power of 2).

This table confirms our intuition that high values of *m* result in a high number of operations, and that complexity increases somewhat proportionally to this value. Note that the last five sets of parameters were not broken in practice and the estimated complexity is always quite high. It is not clear what the authors mean by

The fourth set of parameters seem to contradict our intuition, since it was broken in practice with relative ease even though

The authors did not present any explicit result against GS codes and, in particular, it is not known whether a folded GS code is still a GS code. Thus, the attack in this case is limited to solving the generic system

#### Norm-trace codes.

An attack based on *norm-trace codes* has been recently introduced by Barelli and Couvreur [4]. As the name suggests, these codes are the result of the application of both the trace and the norm operation to a certain support vector, and they are alternant codes. In particular, they are subfield subcodes of Reed–Solomon codes. The construction of these codes is given explicitly only for the specific case

where α is an element of trace 1.

The main idea of the attack is that there exists a specific norm-trace code that is the *conductor* of the secret subcode into the public code. By “conductor” the authors refer to the largest code for which the Schur product (i.e. the component-wise product of all codewords, denoted by

The authors present two strategies to determine the secret subcode. The first strategy is essentially an exhaustive search over all the codes of the proper co-dimension. This co-dimension is given by *s* is the size of the permutation group of the code, which is non-trivial in our case due to the code being quasi-dyadic. While such a brute force in principle would be too expensive, the authors present a few refinements that make it feasible, which include an observation on the code rate of the codes in use, and the use of shortened codes.

The second strategy, instead, consists of solving a bilinear system, which is obtained using the parity-check matrix of the public code and treating as unknowns the elements of a generator matrix for the secret code (as well as the support vector

In any case, it is easy to deduce that the two parameters *q* and *s* are crucial in determining the cost of running this step of the attack, which dominates the overall cost. In fact, the authors are able to provide an accurate complexity analysis for the first strategy which confirms this intuition. The average number of iterations of the brute force search is given by *c* is exactly the co-dimension described above, i.e. *q*-ary operations can be done in constant time (using tables) when *q* is not too big. All this leads to a complexity which is below the desired security level for all of the DAGS parameters that had been proposed at the time of submission. We report these numbers in Table 3.

Early DAGS parameters. * Claimed.

Security level* | q | m | n | k | s | t | w | Attack | |

1 | 2 | 832 | 416 | 43 | 13 | 104 | |||

3 | 2 | 1216 | 512 | 43 | 11 | 176 | |||

5 | 2 | 2112 | 704 | 43 | 11 | 352 |

As it is possible to observe, the attack complexity is especially low for the last set of parameters since the dyadic order *s* was chosen to be

Unfortunately, the attack authors were not able to provide a security analysis for the second strategy (bilinear system). This is due to the fact that the attack uses Gröbner based techniques, and it is very hard to evaluate the cost in this case (similarly to what happened for FOPT). In this case then, the only evidence the authors provide is experimental, and based on running the attack in practice on all the parameters. The authors report running times around 15 minutes for the first set and less than a minute for the last, while they admit they were not able to complete the execution in the middle case. This seems to match the evidence from the complexity results obtained for the first strategy, and suggests a speedup proportional to those. Further test runs are currently planned, but the fact that the attack already fails to run in practice for the middle set, gives us some confidence to believe that updated parameters will definitely make the attack infeasible.

### 5.4 Parameter selection

To choose our parameters, we keep in mind all the remarks from the previous sections about decoding attacks and structural attacks. As we have just seen, we need to respect the condition *q* has to be chosen large enough and *s* cannot be too big. Finally, for ISD to be computationally intensive, we require a sufficiently large number *w* of errors to decode. This is given by

In addition, we tune our parameters to optimize performance. In this regard, the best results are obtained when the extension degree *m* is as small as possible. This, however, requires the base field to be large enough to accommodate sufficiently big codes (against ISD attacks), since the maximum size for the code length *n* is capped by *s* is constrained to be a power of 2, and that odd values of *t* seem to offer best performance.

Putting all the pieces together, we are able to present three sets of parameters, in Table 4. These correspond to three of the security levels indicated by NIST (first column), which are related to the hardness of performing a key search attack on three different variants of a block cipher, such as AES (with key length respectively 128, 192 and 256). As far as quantum attacks are concerned, we claim that ISD with Grover (see above) will usually require more resources than a Grover search attack on AES for the circuit depths suggested by NIST (parameter MAXDEPTH). Thus, classical security bits are the bottleneck in our case, and as such we choose our parameters to provide 128, 192 and 256 bits of classical security for security levels 1, 3 and 5 respectively. For practical reasons, during the rest of the paper we will refer to these parameters respectively as DAGS_1, DAGS_3 and DAGS_5.

Suggested DAGS parameters.

Security level | q | m | n | k | s | t | w | BC | ||

1 | 2 | 832 | 416 | 43 | 13 | 104 | 25 | |||

3 | 2 | 1216 | 512 | 32 | 11 | 176 | 21 | |||

5 | 2 | 1600 | 896 | 32 | 11 | 176 | 21 |

For the above parameters, it is easy to observe that the value

## 6 Performance analysis

### 6.1 Components

DAGS operates on vectors of elements of the finite field *q* is a power of 2 as specified by the choice of parameters. Finite field elements are represented as bit strings using standard log/antilog tables (see for instance [29, Chapter 4, Section 5]) which are stored in the memory.

For DAGS_1, the finite field *conversion matrix* to switch between different field representations. Similarly, for DAGS_3 and DAGS_5, we build the base field using

### 6.2 Randomness generation

The randomness used in our implementation is provided by the NIST API. It uses AES as a PNGR, where NIST chooses the seed in order to have a controlled environment for tests. We use this random generator to obtain our input message *KangarooTwelve* function [48] from the Keccak family. To generate a low-weight error vector, we take part of *KangarooTwelve* to expand the seed into a string of length *n*, then transform the latter into a fixed-weight string using a deterministic function.

### 6.3 Efficient private key reconstruction and decoding

As mentioned in Section 3, in our scheme we use a standard alternant decoder (step (2) of Algorithm 3), which requires the input to be a matrix in alternant form, i.e. *stn* elements of

- (1)Input received word
to be decoded.$\bm{c}$ - (2)Compute the vector
.$\bm{s}=\mathrm{Diag}\left(\bm{y}\right)\cdot {\bm{c}}^{T}$ - (3)Form intermediate matrix
. To do this:$\stackrel{~}{H}$ - (3)(a)Set first row equal to
.$\bm{s}$ - (3)(b)Obtain row
*i*, for , by multiplying the$i=1,\dots ,st-1$ *j*-th element of row by$i-1$ , for${v}_{j}$ .$j=0,\dots ,n-1$

- (3)(a)Set first row equal to
- (4)Sum elements in each row and output resulting vector.

### 6.4 Measurements

The implementation is in ANSI C, as requested for a generic reference implementation. For the measurements, we used an x64 Intel Core i5-5300U processor at 2.30 GHz, 16 GiB of RAM and GCC version 6.3.0 20170516 without any optimization, running on Debian 9.2.

We start by considering space requirements.
We recall the flow between two parties

When instantiated with DAGS, the public key is given by the generator matrix *G*. The non-identity block *s*, thus requires only *q*-ary vector of length *n* plus 256 bits. This leads to the measurements (in bytes) given in Table 5 and Table 6.

Memory requirements.

Parameter set | Public key | Private key | Ciphertext |

DAGS_1 | 8112 | 2496 | 656 |

DAGS_3 | 11264 | 4864 | 1248 |

DAGS_5 | 19712 | 6400 | 1632 |

Communication costs in protocol flow.

Message flow | Transmitted message | Size | ||

DAGS_1 | DAGS_3 | DAGS_5 | ||

G | 8112 | 11264 | 19712 | |

656 | 1248 | 1632 |

We now move on to analyzing time measurements. We are using x64 architecture and our measurements use an assembly instruction to get the time counter. We do this by calling “rdtsc” before and after the instruction, which gives us the cycles used by each function. Table 7 gives the results of our measurements represented by the mean after running the code 50 times.

Timings.

Cycles | |||

Algorithm | DAGS_1 | DAGS_3 | DAGS_5 |

Key Generation | 2540311986 | 4320206006 | 7371897084 |

Encapsulation | 12108373 | 26048972 | 96929832 |

Decapsulation | 215710551 | 463849016 | 1150831538 |

### 6.5 Comparison

We thought it useful to provide a comparison with other recently proposed code-based KEMs (and in particular, NIST submissions). In Table 8, we present data for Classic McEliece, BIKE and BIG QUAKE with regards to memory requirements, for the highest security level (256 bits classical). We did not deem necessary, on the other hand, to provide a comparison in terms of implementation timings, as reference implementations are designed for clarity, rather than performance.

It is easy to see that the public key is much smaller than Classic McEliece and BIG QUAKE, and similar to that of BIKE. With regards to the latter, note that, for the same security level, the total communication bandwidth is of the same order of magnitude. This is because DAGS uses much shorter codes, and as a consequence the ciphertext is considerably smaller than a BIKE ciphertext. Moreover, for the purposes of a fair comparison, we remark that BIKE uses ephemeral keys, has a non-negligible decoding failure rate and only claims IND-CPA security – all factors that can restrict its use in various applications.

Comparison of code-based KEMs (bytes).

Parameter set | Public key | Private key | Ciphertext |

Classic McEliece | 1047319 | 13908 | 226 |

BIKE-1 | 8187 | 548 | 8187 |

BIKE-2 | 4093 | 548 | 4093 |

BIKE-3 | 9032 | 565 | 9032 |

BIG QUAKE | 149800 | 41804 | 492 |

DAGS_5 | 19712 | 6400 | 1632 |

## 7 Conclusion

In this paper, we presented DAGS, a key encapsulation mechanism based on quasi-dyadic generalized Srivastava codes. We proved that DAGS is IND-CCA secure in the random oracle model, and in the quantum random oracle model. Thanks to this feature, it is possible to employ DAGS not only as a key exchange protocol (for which IND-CPA would be a sufficient requirement), but also in other contexts such as hybrid encryption, where IND-CCA is of paramount importance.

In terms of performance, DAGS compares well with other code-based protocols, as shown by Table 8 and the related discussion (above). Another advantage of our proposal is that it does not involve any decoding error. This is particularly favorable in a comparison with some lattice-based schemes like [15], [2] and [14], as well as BIKE. No decoding error allows for a simpler formulation and better security bounds in the IND-CCA security proof.

Unlike traditional code-based protocols, DAGS features small sizes for all components, that is ciphertexts, private keys (thanks to our improved computation idea) and public keys. All the objects involved in the computations are vectors of finite fields elements, which in turn are represented as binary strings; thus computations are fast. The cost of computing the hash functions is minimized thanks to the parameter choice that makes sure the input

The current reference code for the scheme is available at the repository https://git.dags-project.org/dags/dags. Our team is currently at work to complete various implementations that can better showcase the potential of DAGS in terms of performance. These include code prepared with x86 assembly instructions (AVX) as well as a hardware implementation (FPGA) etc. A hint at the effectiveness of DAGS can be had by looking at the performance of the scheme presented in [16], which also features an implementation for embedded devices. In particular, we expect DAGS to perform especially well in hardware, due to the nature of the computations of the McEliece framework.

Finally, we would like to highlight that a DAGS-based key exchange features an “asymmetric” structure, where the bandwidth cost and computational effort of the two parties are considerably different. In particular, in the flow described in (6.1), the party

In light of all these aspects, we believe that DAGS is a promising candidate for post-quantum cryptography standardization as a key encapsulation mechanism.

# A Note on the choice of ω

As discussed in Section 6.3, in our scheme we use a standard alternant decoder. After computing the syndrome of the word to be decoded, the next step is to recover the error locator polynomial *i* if and only if

Of course, if one of the

Now, the generation of the error vector is random, hence we can assume the probability of having an error in position *i* to be around *mst* is close to *m*; however, we still argue that the code is not fully decodable and we now explain how to adapt the key generation algorithm to ensure that all the

As part of the key generation algorithm we assign to each

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## Footnotes

^{1}

DAGS is not only an acronym but also one of the names for the Elder Futhark rune pictured above. The shape of the rune recalls the dyadic property of the matrices at the core of our scheme.

^{2}

Making sure to exclude any block containing an undefined entry.

^{3}

See Appendix A for restrictions about the choice of the offset.

^{4}

See Section 6.3.

^{5}

Respectively,

^{6}

Same as in Theorem 4.4.