Papers for

cryptography developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Ideal lattice problems shown as hard as generic lattice problems

NP-hardness of ideal lattice problems

Abstract: We establish the worst-case hardness of several ideal lattice problems (including SVP and CVP) in the $\ell_2$ norm by providing a dimension-preserving, deterministic polynomial time reduction from their generic lattice versions. The reduction constructs an ideal lattice in the canonical embedding of a number field that approximates some input lattice up to scaling and orthogonal transformation. The integers defining the ideal and the ambient number ring, in particular its discriminant, are all polynomial in bit length relative to the generic input lattice. Furthermore, the ideal is invertible, the ring is monogenic, and the number field is totally real. If the number ring is also required to be a full ring of integers, the reduction conjecturally succeeds in bounded-error quantum polynomial time.

Mon 14 SeptComputational Complexity
The gist
Certain problems involving ideal lattices, which are special mathematical structures, are as difficult to solve as their more general lattice counterparts. The authors found a way to transform these general lattice problems into ideal lattice problems without making them easier to solve, using an efficient and deterministic method. This means that breaking these ideal lattice problems is at least as hard as breaking the generic ones, which is important in cryptography and computational theory. They also consider quantum computing scenarios for related constructions.
Open 2609.15813v1

Deterministic algorithm improves permanent matrix approximation accuracy

Subexponential Approximation of the Permanent in Deterministic Polynomial Time

Abstract: We give the first deterministic polynomial time algorithm that approximates the permanent of arbitrary nonnegative rational matrices within a subexponential factor. For a matrix of order $n$, the approximation factor is \[ \exp\!\left(O\!\left(\frac{n(\log\log n)^2}{\log n}\right)\right)=\exp(o(n)). \] All previously known deterministic polynomial time guarantees for unrestricted inputs had approximation factors $\exp(Ω(n))$. Our proof uses convex optimization to tighten an upper bound on the permanent. The bound is based on weighted sums over all matchings in a bipartite graph representing the matrix, and correlations between unmatched vertices control its error. We approximate these sums deterministically using correlation decay and a bound on the effect of vertex deletion.

Wed 9 SeptData Structures and AlgorithmsComputational Complexity
The gist
Calculating the permanent of a matrix is important in math and computer science but is very hard to do exactly. This paper presents a new algorithm that can approximate the permanent for any nonnegative matrix much more closely than before using a fast, predictable method. The key idea is to use clever math tools to better estimate important sums related to the matrix, improving previous methods that had much larger errors. This makes it possible to get a good approximation in a reasonable amount of time for bigger problems than before.
Open 2609.10516v1

Sparse polynomial gcd algorithms achieve linear complexity in key parameters

Sparse Polynomial GCD Algorithms Asymptotically Linear in All Fundamental Parameters

Abstract: Let $A, B \in \mathbb{Z}[x_1, \dots, x_n]$ be multivariate polynomials with integer coefficients and let $G = \gcd(A, B)$. We present an algorithm for computing $G$ whose expected bit complexity is asymptotically linear in all fundamental parameters: the number of variables $n$, the term count $T = \max\{\|A\|_0, \|B\|_0, \|G\|_0\}$, the total degree $D$, and the logarithmic coefficient sizes $\log\Hi$ and $\log\Ho$, where $\Hi$ bounds the coefficients of the inputs and $\Ho$ bounds those of the GCD. The bit complexity is characterized by the clean bound \[ \widetilde{O}\bigl( n \cdot T \cdot D \cdot \log\Hi \cdot \log\Ho \bigr). \] To our knowledge, this is the first sparse GCD algorithm over the integers that achieves linear complexity in all these parameters simultaneously. The integer algorithm is built upon a new field GCD algorithm. For $A, B \in \K[x_1, \dots, x_n]$ over a field $\K$ with $\operatorname{char}(\K) = 0$ or $\operatorname{char}(\K) > °G$, we give the first algorithm that computes $G = \gcd(A,B)$ with expected \[ \widetilde{O}\bigl( n \cdot T \cdot D \bigr) \] field operations, which is both input- and output-sensitive. The key technical contribution behind both algorithms is a derivative-aided separated Hensel lifting technique introduced in this paper. By introducing an auxiliary variable and leveraging derivative information, our scheme extracts all partial exponents via a single $z^2$-lift per variable, achieving constant sequential depth $O(1)$. This stands in sharp contrast to classical Hensel lifting, which requires $O(D)$ sequential lifting steps and suffers from representation densification in the sparse setting. The field algorithm is then extended to the integer case through modular reduction and rational reconstruction.

Tue 8 SeptData Structures and Algorithms
The gist
Calculating the greatest common divisor (GCD) of two complicated polynomials with many variables and large coefficients is usually slow and hard. This paper introduces new algorithms that speed up this process, making it roughly proportional to the size and complexity of the polynomials. The authors use a clever method that leverages derivatives and a special lifting technique, reducing what used to take many steps to just a few. This breakthrough works both for polynomials with coefficients in fields and those with integer coefficients.
Open 2609.08074v1

Polynomial time tests for surjective maps between certain finite groups

Algorithms for Finite Group Epimorphism Testing

Abstract: The Group Epimorphism Problem (GpEpi) asks, given two finite groups $G_1$ and $G_2$, whether there exists a surjective group homomorphism, or epimorphism, from $G_1$ to $G_2$. When the input groups are given by their multiplication (Cayley) tables, the problem admits a quasipolynomial-time algorithm in general, but little is known about its complexity for structured classes of finite groups. In this paper, we study the computational complexity of GpEpi for several well-studied classes of finite groups. Our main results are polynomial-time epimorphism tests for several classes of groups for which polynomial-time isomorphism testing was previously known: Groups with Abelian normal Hall subgroups with cyclic complement; Groups with (product of) elementary Abelian normal Hall subgroup with elementary Abelian complement; and Groups with some constraints on their Abelian chief factors.

Mon 7 SeptData Structures and AlgorithmsComputational Complexity
The gist
The problem studied is how to decide if you can map one finite group onto another in a way that covers every element of the second group (called a surjective homomorphism). The authors look at groups with special internal structure and develop algorithms that decide this question quickly, in time that grows reasonably with the size of the groups. These results build on previous work that could quickly test if two such groups were basically the same (isomorphic). Their methods work for several important types of groups that mathematicians often study.
Open 2609.07429v1