Differential biases, -differential uniformity, and their relation to differential attacks
Abstract
Differential cryptanalysis famously uses statistical biases in the propagation of differences in a block cipher to attack the cipher. In this paper, we investigate the existence of more general statistical biases in the differences. To this end, we discuss the -differential uniformity of S-boxes, which is a concept that was recently introduced in Ellingsen et. al. [IEEE Transactions on Information Theory, vol. 66, no. 9 (2020)] to measure certain statistical biases that could potentially be used in attacks similar to differential attacks. Firstly, we prove that a large class of potential candidates for S-boxes necessarily has large -differential uniformity for all but at most choices of , where is a constant independent of the size of the finite field . This result implies that for a large class of functions, certain statistical differential biases are inevitable.
In a second part, we discuss the practical possibility of designing a differential attack based on weaknesses of S-boxes related to their -differential uniformity.
Index Terms:
Differential attack, Substitution-Permutation Network, -Differential Uniformity.I Introduction
I-A Background on block ciphers and differential cryptanalysis
Block ciphers
A block cipher is a symmetric encryption scheme that transforms an -bit plaintext into an -bit ciphertext using a secret key. Block ciphers (like AES) constitute the majority of all symmetric ciphers in use today, and are a staple of modern cryptography. A classical construction of block ciphers is iterative, meaning that the entire cipher is a sequence of (generally simple and almost identical) round functions, see Figure 1 for a schematic illustration. A standard choice for a round function is a Substitution-Permutation Network (SPN), which consisting of a linear layer, sometimes called the permutation part and a substitution layer (usually several so called S-boxes running in parallel), with a key addition in between the rounds, see Figure 2 for a conceptual visualization.
Differential cryptanalysis
One of the most important attacks against block ciphers is the so-called differential attack introduced in [1], which also serves as a basis for more advanced attacks like boomerang attacks [2], rectangle attacks [3], or differential-linear attacks [4].
Let us briefly recall the main idea behind the classical differential attack. The attack uses that differences of plain texts propagate with different probabilities through the encryption process.
A cipher vulnerable to a differential attack has a (strongly) non-uniform distribution of differences that can then be exploited by a differential attack. The concept of difference used here is usually the XOR (i.e. addition in ). The main reason for this is that the most standard design for SPNs uses key additions, i.e. the key is simply added to the message in between all rounds.
Clearly, we have , so the key addition process does not impact the propagation of differences at all. Further, the differences are also not impacted by the linear layer of SPNs, so the only part that has direct influence is the S-box layer, which makes both analysis and design considerably easier. For a function , the differential attack thus considers the following distribution of probabilities, where , are the differences of plain and cipher texts, respectively:
(1) |
The main idea behind the differential attack is then that, because the key does not impact the propagation of differences, differences can be broken down over all rounds, so differential trails can be constructed which capture the probability that an input difference propagates through an -round block cipher to an output difference via intermediate differences after the -th round. With the tacit assumption of uniformity, the differential analysis of the cipher can thus be broken down to analysing its constitutive rounds, which, in the case of an successful attack, allows the attacker to guess the key given enough plaintext - ciphertext pairs. To combat these differential attacks, the probabilities in Eq. (1) should be as uniform as possible.
Differential uniformities of S-boxes
As described above, the resistance of an SPN relies on its non-linear part, i.e. the choice of the S-box. The behaviour of an S-box with regard to differential attacks is measured by its Difference Distribution Table (DDT) and differential uniformity which are defined as
(2) | ||||
(3) |
The lower the differential uniformity of , the better is its resistance against a differential attack.111Of course, the choice of the linear layer is also important for the resistance against a differential attack to maximize the number of active S-boxes. The best possible differential uniformity for S-boxes over binary finite fields is 2, those S-boxes are called almost perfect nonlinear (APN).
Alternative differences and -differential uniformities
The basic idea of the differential attack can be transferred to another group operation that substitutes the role of the XOR/addition. For example, multiplicative differentials were considered in [5]. Instead of investigating the propagation on pairs , this kind of differential attack considers pairs , where the multiplication is the multiplication in the ring . These kind of attacks were motivated by a number of ciphers that did not just use a simple key addition, but instead relied on modular multiplication as a primitive operation. The authors of [6] use this motivation to consider a notion that tracks some statistical behaviour of the propagation of differences and is clearly inspired by the definition of the differential uniformity in (3). The definition uses a more general setting (not restricting to characteristic ), and uses the well known isomorphism (as vector spaces) of and .
Definition I.1.
[6, Definition 1] Let , and . For , we let the entries of the -Difference Distribution Table (-DDT) be defined by . We call the quantity
the -differential uniformity of .
In characteristic , the case clearly recovers the usual definition of DDT and differential uniformity in (2) and (3). Again, the function has minimal statistical bias if the -differential uniformity is as low as possible. If a function achieves the minimal possible -differential uniformity , we call it perfect -nonlinear (PcN).
Remark I.2.
Since our investigations in this paper are largely independent of the characteristic of the underlying finite field, following [6], we chose to discuss the -differential uniformity and its related concepts in full generality in all positive characteristics. We want to note that while symmetric cryptography in odd characteristic remains a rarity, there are some recent developments in this direction [7].
The new notion of -differential uniformity has led to much new research, counting more than a dozen papers in less than two years dedicated to it (see e.g. [8, 9, 10], and in particular the survey paper [11] and the references therein), usually focusing on determining the -differential uniformity of functions that have seen use or are possible candidates for S-boxes. Despite the considerable attention that the -differentials have received, a practical analysis has so far been lacking.
I-B Our Contribution and organization of the paper
The study of the -differential uniformity serves as an example of two important and much more general questions for the design of block ciphers:
-
•
Is it possible to give general conditions when statistical biases for functions of a certain pattern are guaranteed?
-
•
If we know that a function has a statistical bias of a certain form, can we find a framework to analyze the possibility of a practical attack using this bias?
In this paper, we are going to give answers to these two questions using the example of biases encoded by -differential uniformities.
We want to emphasize that the purposes of this paper is to show that certain differential biases are essentially inevitable, and to provide a general discussion on whether it is possible to mount attacks based on those biases. It remains an open question whether these biases (that provably exist) can be used to mount a specific attack on a cipher that is currently in use.
Broadly speaking, the contribution of this paper is thus twofold: In Section II, we show that for a wide class of functions the -differential uniformity is actually the worst possible for almost all . More precisely, we show in Theorem II.18 that in characteristic there is an explicit constant , independent of the size of the finite field , such that for all ’s outside a set of size any function of odd degree whose first and second Hasse derivative is non-zero has worst possible -differential uniformity. Note that functions with second Hasse derivative equal to zero are highly non-generic and can be completely characterized (see Remark II.19). The proofs in this section use novel techniques for this line of research, relying on the theory of algebraic function fields, Galois theory, and algebraic geometry.
The results of Section II indicate that in many cases, some extreme statistical biases in the -differences are actually inevitable. This result of course leads to the obvious question whether or how one can use those biases to mount an attack on a cipher.
In Section III, we discuss the possibility of constructing such an attack. In particular, we investigate what kind of cipher could be theoretically vulnerable to an attack based on a statistical weakness in the -differential uniformity and we relate the -differential uniformity to a special kind of "regular" differential attack. To do this, we introduce a general form of differential uniformity using a (more arbitrary) binary operation replacing the usual XOR. We in particular investigate the interaction of these generalized differences with the linear layer and the key addition process of a standard SPN.
We finish our paper with a conclusion and some interesting practical and theoretical questions related to our results.
II On the -differential uniformity of low degree polynomials
Whereas most of the papers that appeared so far in the literature on -differential uniformities deal with monomials (see e.g. [12, 6, 8, 13, 14, 15, 16]), in this section, we investigate necessary constraints on polynomials with low (i.e. good) -differential uniformity. We start with a basic overview over the tools we employ to get the result.
II-A Tools and Notation
Global Function Fields and Galois Theory
We give a brief overview on function fields and Galois theory to the extent that is needed for this section. We broadly follow the notation from [17].
Let be the rational function field in the variable over the finite field of order . A function field is an algebraic extension of . The field of constants of is the subfield of elements of that are algebraic over .
A valuation ring is a subring of that contains , is different from , and such that if , then . A place is the unique maximal ideal of a valuation ring and it is principal. The valuation ring attached whose maximal ideal is the place is denoted by .
Fix a place . Each element can be uniquely written as , where is a prime element for a place and is invertible. We associate to a function , called valuation at , defined as , if , and , if .
An inclusion of function fields is said to be a function field extension, and we will denote it by . The degree of is simply the integer . If is a place of , there are places of above , i.e. places of that contain . The relative degree of over is the integer .
The ramification index of over is a natural number such that
We say that is ramified if , and unramified if . The fundamental equality for function field extensions states
A finite separable extension of fields is said to be Galois if has size . Let us recall that every splitting field of a separable polynomial in is a Galois extension of . Moreover, every Galois extension of can be seen as a splitting field of some polynomial in .
Definition II.1.
Let be a Galois extension of function fields and let be a place of lying over a place of . Then we define the decomposition group as
and the inertia group as
The following result is useful to connect the action of the Galois group on the roots to the intermediate splitting of places. See [18, Satz 1].
Lemma II.2.
Let be a finite separable extension of function fields, let be its Galois closure and be its Galois group. Let be a place of and be the set of places of lying above . Let be a place of lying above . Then we have the following:
-
1.
There is a natural bijection between and the set of orbits of under the action of the decomposition group .
-
2.
Let and let be the orbit of corresponding to . Then where and are ramification index and relative degree, respectively.
-
3.
The orbit partitions further under the action of the inertia group into orbits of size .
It follows immediately that
Corollary II.3.
If is ramified at then is non-trivial. In particular, is ramified at if and only if is ramified at .
Notice that if is a Galois extension of function fields over , then is Galois for any extension of (including . It is well known, see for example [17], that the geometric Galois group is generated by the inertia groups.
Lemma II.4.
Let be a finite separable extension of function fields, let be its Galois closure and let . Then is generated by the inertia groups , i.e.
The following result can be deduced from [19].
Theorem II.5.
[20, Theorem 3.3] Let be a prime number, a positive integer, and . Let be a separable extension of global function fields over of degree , be the Galois closure of , and suppose that the field of constants of is . There exists an explicit constant depending only on the genus of and the degree of such that if then has a totally split place.
The following corollary shows that we can deduce arithmetic information (splitting over ) from geometric information (splitting over , i.e. ramification).
Corollary II.6.
Let be a prime number, a positive integer, and . Let be a separable extension of global function fields over of degree , be the Galois closure of . Then if , we have that has a totally split place (over ).
Proof.
It is a standard fact that the Galois group of is equal to the Galois group of , where is the constant field of . Therefore, since , we have that by the Galois correspondence, and the final claim follows using Theorem II.5.
∎
The following result follows from [21, Proposition 1, Page 275].
Proposition II.7.
Let be a transitive subgroup of generated by transpositions. Then .
Curves and Varieties over Finite Fields
As a notation, and denote the projective and the affine space of dimension over the finite field , a prime power. In the cases and we denote by and the line and the plane at infinity respectively. A variety and more specifically a curve, i.e. a variety of dimension 1, is described by a certain set of equations with coefficients in a finite field . We say that a variety is absolutely irreducible if there are no varieties and defined over the algebraic closure of and different from such that . If a variety is defined by homogeneous polynomials , for , an -rational point of is a point such that , for . A point is affine if . The set of the -rational points of is usually denoted by . We denote by the same symbol homogenized polynomials and their dehomogenizations, if the context is clear. For a more comprehensive introduction to algebraic varieties and curves we refer to [22, 23].
In this paper we will mostly make use of hypersurfaces, i.e varieties in of dimension , and specifically curves () and surfaces (). Any hypersurface is defined by a unique homogeneous polynomial in variables. For the sake of convenience, for a hypersurface we will also make use of its affine equation .
Singular points of algebraic curves and surfaces can be investigated via the so-called Hasse derivative; see also [22, Page 148].
Definition II.8 ([24]).
Let be a polynomial. For any , can be written uniquely as
for some polynomials . For a given multi-index , we define the -th Hasse derivative of as the polynomial .
It can be seen that for any monomial its -th Hasse derivative is
and vanishes if for some ; see also [25].
As a notation, if a polynomial is univariate, we denote its derivatives as , , ….
Let be a polynomial defining an affine plane curve , let be a point in the plane, and write
where is either zero or homogeneous of degree .
The multiplicity of , written as , is the smallest integer such that and for ; the polynomial is the tangent cone of at . A linear divisor of the tangent cone is called a tangent of at . The point is on the curve if and only if . If is on , then is a simple point of if , otherwise is a singular point of . A quick criterion to decide whether an affine point is singular is the following: is singular if and only if .
It is possible to define in a similar way the multiplicity of an ideal point of , that is a point of the curve lying on the line at infinity.
Given two plane curves and and a point on the plane, the intersection number of and at the point can be defined by seven axioms. We do not include its precise and long definition here. For more details, we refer to [26] and [22] where the intersection number is defined equivalently in terms of local rings and in terms of resultants, respectively.
Concerning the intersection number, the following two classical results can be found in most of the textbooks on algebraic curves.
Lemma II.9.
Let and be two plane curves. For any affine point , the intersection number satisfies the inequality
with equality if and only if the tangents at to are all distinct from the tangents at to .
Theorem II.10 (Bézout’s Theorem).
Let and be two projective plane curves over an algebraically closed field , having no component in common. Let and be the polynomials associated with and respectively. Then
where the sum runs over all points in the projective plane .
Concerning surfaces in , i.e. variety of dimension 2 defined by a homogeneous polynomial , the same definitions for singular points and multiplicity hold. In particular a point is singular for a surface if and only if
The following is a simple result about the irreducibility of plane sections of absolutely irreducible surfaces. We include here its proof for the sake of clarity.
Proposition II.11.
Let be an absolutely irreducible surface and consider a plane . A singular point for the curve is either a singular point for or is the tangent plane to at . In particular, if is reducible then either is the tangent plane at some point of or contains a singular point of .
Proof.
Without loss of generality we can suppose that is the origin and and that , for some polynomial with . This means that the affine equation of is of type for some . Now, either there exists a constant term in and thus is nonsingular for and is the tangent plane at to or possesses monomials of degree at least one and thus is a singular point for .
The second part of the claim directly follows, observing that if is reducible then the curve possesses singular points (possibly defined over ).
∎
Finally, we include here references for estimates on the number of -rational points of algebraic varieties over finite fields. The most celebrated result is the Hasse-Weil Theorem.
Theorem II.12.
[Hasse-Weil bound for curves] Let be a projective absolutely irreducible non-singular curve of genus defined over . Then
(4) |
If is a non-singular plane curve, then , where is the degree of the curve , and (4) reads
(5) |
If the curve is singular, there is some ambiguity in defining what an -rational point of actually is. Clearly, if is non-singular, then there is a bijection between -rational places (or branches) of the function field associated with and -rational points of . In the singular case, this is no more true. We refer the interested readers to [22, Section 9.6] where other relations are investigated. We point out that actually the bound (5) holds even for singular (absolutely irreducible) curves; [27, Corollary 2.5].
Concerning algebraic varieties of dimension larger than one, the first estimate on the number of -rational points was given by Lang and Weil [28] in 1954.
Theorem II.13.
[Lang-Weil Theorem] Let be an absolutely irreducible variety of dimension and degree . Then there exists a constant depending only on , , and such that
(6) |
II-B Results on the -differential uniformity
In what follows we will consider polynomials , , which are not monomials. Note that the polynomials are not a good choice for an S-box because the map is linear.
The first result we show concerns the 2-transitivity of the geometric Galois group of .
Lemma II.14.
Let , a prime, and , with , , and suppose that is not a monomial. Then, the number of for which there exists such that the geometric Galois group of is not 2-transitive is bounded by an explicit constant independent of .
Proof.
It is well known that the geometric Galois group of is 2-transitive if and only if the curve is absolutely irreducibile; see for instance [35, Theorem 6.11].
Consider the surface
Note that since is not a monomial, is actually a surface and not a curve.
The intersection with the hyperplane at infinity is the curve
and by [36, Theorems 4.1 and 4.2] whenever , where is a primitive -th root of unity in , is nonsingular and therefore absolutely irreducible.
This shows that for any the surface is absolutely irreducible.
From now on we pick up . We want to bound the total number of singular points of . First note that they are only affine since is nonsingular.
A point is singular for only if it is of multiplicity three for , since is of multiplicity one for the denominator . This happens only if
In particular
and thus and there are at most possibilities for .
Consider now singular points of off . In particular, they satisfy
Such a system defines a variety which is the intersection of three surfaces in and is of dimension . To see this it is enough to observe that is precisely the set of of singular points of which is empty. This means that cannot be of dimension larger than otherwise would be positive. This shows that for any the number of singular points of is .
Now we bound the number of tangent planes to of the type . Clearly, if is tangent at then in particular
if and
if . In the former case we already saw that the number of solutions is finite. In the latter case the two plane curves
and
do not share any component since their intersections with the line are disjoint and thus by Bézout’s Theorem the number of intersection points, and thus the number of is . This shows that there is of such that is tangent to . The claim now follows from Proposition II.11.
∎
Remark II.15.
Note that in the lemma above the constant can be taken as .
As a byproduct of the lemma above we can easily deal with the case .
Corollary II.16.
Let , a prime, and , with , , and suppose that is not a monomial. Then, the number of for which can be PN is bounded by an explicit constant independent of .
Proof.
Consider again the surface
In the proof of Lemma II.14 it has already been proved that is absolutely irreducible whenever does not belong to a specific set of values of size at most . By Lang-Weil Theorem the number of affine -rational points of is lower-bounded by
where is an absolute constant depending only on the degree of . The set of parallel planes , , partition the set of its affine -rational points and thus there exists at least an such that . This means that the curve
has at least affine points and thus, since the line is not a component of , possesses at least affine -rational points off and thus is not a permutation polynomial and is not PN.
∎
Remark II.17.
Our main result of this section is the following.
Theorem II.18 (Main Result).
Let , a prime, and , not a monomial, with . Suppose that one of the following holds:
-
1.
, is odd, and both the Hasse derivatives and do not vanish;
-
2.
and .
The number of for which is bounded by an explicit constant independent of .
Remark II.19.
-
•
As it will be clear from the proofs of the ancillary results below, the constant in the statement of Theorem II.18 is smaller than .
-
•
The condition on the Hasse derivatives in Theorem II.18 is not very restrictive. Indeed, the polynomials that violate these conditions can be classified explicitly: The polynomials in such that the first Hasse derivative is zero live in , and the ones for which the second Hasse derivative is zero are exactly the ones of the form
For instance, note that a sufficient condition for which both Hasse derivatives and do not vanish is the existence of at least one monomial in of degree .
-
•
Theorem II.18 states that if is small compared to the field size , it is inevitable that there exist many such that , which is the maximal possible (and thus worst) -uniformity. Note that here refers to the degree of as a polynomial and not to the algebraic degree (which is in the characteristic 2 case equivalent to the highest binary weight of a monomial of ). This means that this result even applies to functions with high algebraic degree since it is clearly possible that a function with high algebraic degree has comparatively low degree as a polynomial.
The proof of Theorem II.18 involves the two surfaces
Note that at this first step we strongly need that the polynomial is not a monomial, otherwise and are curves, being defined by a homogeneous polynomial in three variables, and our arguments do not apply.
Proposition II.20.
Let , . The two surfaces and do not share any component.
Proof.
Consider the two curves and . Then
and
It is readily seen that factorizes as
where runs over the set of the -th roots of unity distinct from .
Let be one of the components of . In order to show that , consider
Since , and thus . So
Since and do not share any component, so do the surfaces and .
∎
Proposition II.21.
Let , , not a monomial, . There exists a set of size at most such that for any there exists at least an such that has at most one multiple root in for any fixed .
Proof.
Consider the system
The solutions of this system correspond to values for which there exist two multiple roots of .
The above system is equivalent to
(7) |
The last two equations define the surfaces and considered in Proposition II.20. Since such surfaces do not share any component, their intersection is of dimension one (i.e. union of curves) and of degree at most .
Thus, apart from a small (at most ) number of , the intersection consists of at most points (on the algebraic closure ). Since , there exists for which the total number of solutions of System (7) is upped bounded by . Let be the set of all the solutions of System (7) for such an and consider . Clearly . Thus, for any we have that has at most one multiple root for any fixed and the claim follows.
∎
Proposition II.22.
Let , , not a monomial, , and . There exists a set of size at most such that for any there exists at least an such that has no root in of multiplicity larger than for any fixed .
Proof.
Consider the system
Any solution provides values such that has a root of multiplicity at least three.
The system above is equivalent to
(8) |
The last equation in System (8) is a non-vanishing equation of degree for any . To see this, let and thus
and the leading coefficient of
is .
Note that and are non-vanishing polynomials. The number of for which and share a factor is upperbounded by . Let be such that and do not share any factor.
For this fixed , System (8) admits at most solutions and the claim follows.
∎
Proposition II.23.
Let and not a monomial. Suppose that both the Hasse derivatives and do not vanish. There exists a set of size at most such that for any there exists at least an such that has no root in of multiplicity larger than for any fixed .
Proof.
The argument is the same as in the proof of Proposition II.22. Now
where and are the largest degrees not divisible by and equivalent to , respectively. So, the leading coefficient of
is and the claim follows.
∎
Proposition II.24.
Let , , and not a monomial, , with
-
1.
if is odd;
-
2.
the Hasse derivatives and do not vanish if .
There exists a set of size at most such that for any there exists at least an such that has either all distinct roots or precisely one double root in for any fixed .
We are now ready to prove the main theorem of this section.
Proof of Theorem II.18.
Using Corollary II.6 it is enough to prove that the number of pairs such that the geometric Galois group of is not the symmetric group , is .
Observe that thanks to Lemma II.2 combined with Proposition II.24 we have that the inertia groups of the Galois group of are all isomorphic to and generated by single transpositions. Since is generated by its inertia groups thanks to Lemma II.4, is a transitive subgroup of generated by transpositions (since is irreducible for any fixed pair ). Proposition II.7 now implies that . Therefore we obtain that as well, from which the claim follows thanks to Corollary II.6.
∎
III The feasibility of differential attacks based on the c-differential uniformity
The focus in the research on -differential uniformity has so far been almost purely on determining the -differential uniformity of specific functions. The actual use case has been mostly neglected, which is surprising given that a clear attack based on bad -differential uniformities has not been presented yet.
While the "standard" differential uniformity measures the probability of a difference propagating through the S-box (indeed is indeed the probability of a difference turning into a difference times ), it is not immediately clear what kind of statistical bias the -differential uniformity measures. Clearly, the distribution of the values of is also a measure of differential bias (since it compares the inputs of and ), but the output is for not a usual difference itself, so the construction of a differential trail that tracks the propagation through several rounds of the cipher is not readily possible. The -differential uniformity thus does not measure a propagation of usual differences.
Unlike the multiplicative differentials [5] mentioned as inspiration for the -differential uniformity in [6], the -differential uniformity also does not measure the propagation of multiplicative "differences" of the form through the cipher since, as mentioned, the input difference used is actually the regular addition. It is however possible to find a kind of "difference" such that the -differential uniformity does measure the propagation of this "difference", as we present now.
III-A A general differential attack
Instead of using the usual difference (which, in the case of the usual setting, boils down to the XOR ), other binary operation can of course be used, for instance in the case of the multiplicative differentials from [5] mentioned above, this is the modular multiplication. So let be a binary operation. We can then consider the propagation of pairs of those generalized differences and, identical to the usual differential attack, we can attempt to use biases in the probabilities of the propagation of those differences.
Generalizing the usual differential uniformity of a function , we can define the -differential uniformity and the -DDT.
Definition III.1.
Let be a function. We define for all
and
Clearly, for one recovers the usual DDT and differential uniformity. But, more interestingly, this framework actually also allows us to recover the -differential uniformity. Indeed, setting for all and a fixed , we get
Since is fixed, a simple transformation then immediately relates the -DDT with the -DDT for this choice of , and the respective differential uniformities are identical. In this sense, the -differential uniformity is just a new special case of differential uniformity for this specific choice of the binary operation . The -differential uniformity thus seems to be just a tool to measure the resistance of a cipher against a specific differential attack based on this operation. We want to note that the general form of differential as described in Definition III.1 was analysed in a series of papers [37, 38] with the idea to find specific binary operations that can lead to efficient differential attacks (or, possibly, for a malicious designer, to a "hidden", non-public binary operation that serves as a trapdoor to a cipher resistant against the usual attacks). However, the authors in those papers only analyse a subclass of binary operations that excludes the specific binary operation that we identified as being related to the -differential uniformity here.
III-B The differential attack based on weak -differential uniformity
Let us now consider a potential differential attack using this specific binary operation defined via for all and . As explained briefly in the introduction on the classic differential attack earlier, the differential attack is possible for many block ciphers since they use simple key addition as a primitive operation, which means that differences propagate in the same way regardless of the key. Moreover, differences propagate also unchanged through the linear layer. We now check if/when those two properties are satisfied by .
We start with a consideration of the linear layer. The following result states that a linear layer applied to the input of the function does not change its -differential uniformity, however a linear layer applied to the output generally does.
Theorem III.2.
Let be a function and be an -affine permutation, where and is the linear part of . Then
and in particular
Moreover, if is affine over where , then
and
However, generally if .
Proof.
Clearly, if and only if , and the first results follows since is a permutation.
On the other hand, if and only if
If for all , then and . We check when occurs. Writing as a polynomial , we get
Comparing coefficients yields for all with . is equivalent to or , so we conclude that for all if and only if unless or which implies that unless or . This shows that is linear over .
If this condition does not hold, it is easy to construct examples by computer search that show .
∎
Theorem III.2 shows that unless one picks very specific linear layers or (which in the case most interesting for applications only holds for , i.e. the classical differential uniformity), the -differential uniformity is actually affected by the choice of the linear layer. In particular, unlike the classical differential attack, the resistance of the cipher cannot be broken down to the S-box level, since linear layers used in block ciphers are of course generally not linear over extension fields.
Let us now consider the interaction of the -differences with the process of key addition. For the classical differences, the key addition does not impact any differences since
(9) |
for any choice of . For our binary operation , let , defined as , be the "inverse" of in the sense that for all . We now consider the differences with respect to by substituting and of the classical differences in Eq. (9) with and (while of course keeping the regular addition for the key addition). This yields
It is immediate to see that the differences now are neither independent from the subkey nor from the message if .
So, in closing, it seems that a differential attack based on -differences (attempting to abuse high -differential uniformity) has several practical challenges as both the linear layers and the key addition process used in the vast majority of block ciphers make such an attack considerably harder than an attack relying on the classical concept of differences. For block ciphers that do not use a simple key addition but possibly another primitive operation, differential attacks based on different binary operations as lined out in this section might be of practical interest.
Regardless, the -differential uniformities still measure biases in the distribution of differences and it might still theoretically be possible to construct an attack different than the one considered here to abuse this bias. However, it seems to be clear that an analysis would be made considerably more difficult by the fact that linear layers play a more significant role than in the classical differential attack and its derivatives.
IV Conclusions and open problems
Our main contribution concerns the -differential uniformity of polynomials and states that for a generic polynomial there exists only a thin set of instances of for which is not the worst possible. Our investigation involves techniques from both Algebraic Geometry in positive characteristic and Galois Theory and tells us that in order to avoid the differential biases encoded in the -differential uniformity, polynomials need to respect specific constraints on their degree structure.
More generally, we show that extreme statistical differential biases for a big class of functions are inevitable. While our analysis in Section III indicates that constructing attacks based on those biases is not easy, it remains open if other attacks exploiting -differential uniformities are possible.
An obvious question is if it is possible to extend the techniques we used in this paper to analyze other statistical biases, in particular it would be interesting to see if results of the form of Theorem II.18 can be achieved.
An interesting theoretical open question concerns the possibility to obtain similar results involving weaker constraints, for instance dropping the condition in Theorem II.18.
In Section III, we discussed a general differential attack with an arbitrary binary operation replacing the XOR. In [37], the authors investigate a similar generalized attack (starting from a different motivation), and argue that for some specific binary operations, there are enough weak keys that allow the exploitation of certain biases. While the results are not applicable to the case of -differential uniformities, it would be interesting if the operations investigated in [37] behave similarly to the -differential uniformity as described in this paper.
V Acknowledgments
The first author is supported by the Italian National Group for Algebraic and Geometric Structures and their Applications (GNSAGA - INdAM). The second and third authors are supported by NSF grant 2127742.
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Daniele Bartoli was born in Molfetta, Italy, on September 22, 1985. He received the degree in Mathematics from the University of Perugia, Italy, in September 2008 and the Ph.D. degree in Mathematics and Computer Science from the same University in 2012, with a dissertation from coding theory. Currently, he is Associate Professor at the Department of Mathematics and Computer Science, University of Perugia, Perugia, Italy. His research interests are in coding theory, including quantum coding, and Galois geometries. |
Lukas Kölsch reveived the Masters Degree from Otto-von-Guericke University Magdeburg in 2017 and the Ph.D. degree in 2020 from University of Rostock, Germany under the supervision of Gohar Kyureghyan. He is currently a postdoctoral scholar at University of South Florida. He is interested in Boolean functions, symmetric cryptography, and Galois geometry. |
Giacomo Micheli graduated at the University of Rome “La Sapienza” in July 2012. He completed his Ph.D. with distinction at the Zurich Graduate School in Mathematics in October 2015 under the supervision of Prof. Joachim Rosenthal. He is currently a tenure-track assistant professor at the University of South Florida and Co-Director of the Center for Cryptographic Research at USF. |