New Insight into the Isomorphism of Polynomials problem IP1S and its Use in Cryptography

This paper investigates the mathematical structure of the ``Isomorphism of Polynomial with One Secret\u27\u27 problem (IP1S). Our purpose is to understand why for practical parameter values of IP1S most random instances are easily solvable (as first observed by Bouillaguet et al.). We show that the structure of the problem is directly linked to the structure of quadratic forms in odd and even characteristic. We describe a completely new method allowing to efficiently solve most instances. Unlike previous solving techniques, this is not based upon Gröbner basis computations

Polynomial-Time Algorithms for Quadratic Isomorphism of Polynomials: The Regular Case

Let $\mathbf{f}=(f\_1,\ldots,f\_m)$ and $\mathbf{g}=(g\_1,\ldots,g\_m)$ be two sets of $m\geq 1$ nonlinear polynomials over $\mathbb{K}[x\_1,\ldots,x\_n]$ ($\mathbb{K}$ being a field). We consider the computational problem of finding -- if any -- an invertible transformation on the variables mapping $\mathbf{f}$ to $\mathbf{g}$. The corresponding equivalence problem is known as {\tt Isomorphism of Polynomials with one Secret} ({\tt IP1S}) and is a fundamental problem in multivariate cryptography. The main result is a randomized polynomial-time algorithm for solving {\tt IP1S} for quadratic instances, a particular case of importance in cryptography and somewhat justifying {\it a posteriori} the fact that {\it Graph Isomorphism} reduces to only cubic instances of {\tt IP1S} (Agrawal and Saxena). To this end, we show that {\tt IP1S} for quadratic polynomials can be reduced to a variant of the classical module isomorphism problem in representation theory, which involves to test the orthogonal simultaneous conjugacy of symmetric matrices. We show that we can essentially {\it linearize} the problem by reducing quadratic-{\tt IP1S} to test the orthogonal simultaneous similarity of symmetric matrices; this latter problem was shown by Chistov, Ivanyos and Karpinski to be equivalent to finding an invertible matrix in the linear space $\mathbb{K}^{n \times n}$ of $n \times n$ matrices over $\mathbb{K}$ and to compute the square root in a matrix algebra. While computing square roots of matrices can be done efficiently using numerical methods, it seems difficult to control the bit complexity of such methods. However, we present exact and polynomial-time algorithms for computing the square root in $\mathbb{K}^{n \times n}$ for various fields (including finite fields). We then consider \\#{\tt IP1S}, the counting version of {\tt IP1S} for quadratic instances. In particular, we provide a (complete) characterization of the automorphism group of homogeneous quadratic polynomials. Finally, we also consider the more general {\it Isomorphism of Polynomials} ({\tt IP}) problem where we allow an invertible linear transformation on the variables \emph{and} on the set of polynomials. A randomized polynomial-time algorithm for solving {\tt IP} when $\mathbf{f}=(x\_1^d,\ldots,x\_n^d)$ is presented. From an algorithmic point of view, the problem boils down to factoring the determinant of a linear matrix (\emph{i.e.}\ a matrix whose components are linear polynomials). This extends to {\tt IP} a result of Kayal obtained for {\tt PolyProj}.Comment: Published in Journal of Complexity, Elsevier, 2015, pp.3

Solving the "Isomorphism of Polynomials with Two Secrets" Problem for all Pairs of Quadratic Forms

We study the Isomorphism of Polynomial (IP2S) problem with m=2 homogeneous quadratic polynomials of n variables over a finite field of odd characteristic: given two quadratic polynomials (a, b) on n variables, we find two bijective linear maps (s,t) such that b=t . a . s. We give an algorithm computing s and t in time complexity O~(n^4) for all instances, and O~(n^3) in a dominant set of instances. The IP2S problem was introduced in cryptography by Patarin back in 1996. The special case of this problem when t is the identity is called the isomorphism with one secret (IP1S) problem. Generic algebraic equation solvers (for example using Gr\"obner bases) solve quite well random instances of the IP1S problem. For the particular cyclic instances of IP1S, a cubic-time algorithm was later given and explained in terms of pencils of quadratic forms over all finite fields; in particular, the cyclic IP1S problem in odd characteristic reduces to the computation of the square root of a matrix. We give here an algorithm solving all cases of the IP1S problem in odd characteristic using two new tools, the Kronecker form for a singular quadratic pencil, and the reduction of bilinear forms over a non-commutative algebra. Finally, we show that the second secret in the IP2S problem may be recovered in cubic time

Resisting Key-Extraction and Code-Compression: a Secure Implementation of the HFE Signature Scheme in the White-Box Model

Cryptography is increasingly deployed in applications running on open devices in which the software is extremely vulnerable to attacks, since the attacker has complete control over the execution platform and the software implementation itself. This creates a challenge for cryptography: design implementations of cryptographic algorithms that are secure, not only in the black-box model, but also in this attack context that is referred to as the white-box adversary model. Moreover, emerging applications such as mobile payment, mobile contract signing or blockchain-based technologies have created a need for white-box implementations of public-key cryptography, and especially of signature algorithms. However, while many attempts were made to construct white-box implementations of block-ciphers, almost no white-box implementations have been published for what concerns asymmetric schemes. We present here a concrete white-box implementation of the well-known HFE signature algorithm for a specific set of internal polynomials. For a security level $2^{80}$, the public key size is approximately 62.5 MB and the white-box implementation of the signature algorithm has a size approximately 256 GB

On the Complexity of Isomorphism Problems for Tensors, Groups, and Polynomials I: Tensor Isomorphism-Completeness

We study the complexity of isomorphism problems for tensors, groups, and polynomials. These problems have been studied in multivariate cryptography, machine learning, quantum information, and computational group theory. We show that these problems are all polynomial-time equivalent, creating bridges between problems traditionally studied in myriad research areas. This prompts us to define the complexity class TI, namely problems that reduce to the Tensor Isomorphism (TI) problem in polynomial time. Our main technical result is a polynomial-time reduction from d-tensor isomorphism to 3-tensor isomorphism. In the context of quantum information, this result gives multipartite-to-tripartite entanglement transformation procedure, that preserves equivalence under stochastic local operations and classical communication (SLOCC)

On the complexity of isomorphism problems for tensors, groups, and polynomials I: Tensor isomorphism-completeness

We study the complexity of isomorphism problems for tensors, groups, and polynomials. These problems have been studied in multivariate cryptography, machine learning, quantum information, and computational group theory. We show that these problems are all polynomial-time equivalent, creating bridges between problems traditionally studied in myriad research areas. This prompts us to define the complexity class TI, namely problems that reduce to the Tensor Isomorphism (TI) problem in polynomial time. Our main technical result is a polynomial-time reduction from d-tensor isomorphism to 3-tensor isomorphism. In the context of quantum information, this result gives multipartite-to-tripartite entanglement transformation procedure, that preserves equivalence under stochastic local operations and classical communication (SLOCC)