The Differential and Boomerang Properties of a Class of Binomials
- URL: http://arxiv.org/abs/2409.14264v2
- Date: Wed, 25 Sep 2024 18:10:48 GMT
- Title: The Differential and Boomerang Properties of a Class of Binomials
- Authors: Sihem Mesnager, Huawei Wu,
- Abstract summary: We study the differential and boomerang properties of the function $F_2,u(x)=x2big (1+ueta(x)big)$ over $mathbbF_q$.
We disproving a conjecture proposed in citebudaghyan2024arithmetization which states that there exist infinitely many $q$ and $u$ such that $F_2,u$ is an APN function.
- Score: 28.489574654566677
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Let $q$ be an odd prime power with $q\equiv 3\ ({\rm{mod}}\ 4)$. In this paper, we study the differential and boomerang properties of the function $F_{2,u}(x)=x^2\big(1+u\eta(x)\big)$ over $\mathbb{F}_{q}$, where $u\in\mathbb{F}_{q}^*$ and $\eta$ is the quadratic character of $\mathbb{F}_{q}$. We determine the differential uniformity of $F_{2,u}$ for any $u\in\mathbb{F}_{q}^*$ and determine the differential spectra and boomerang uniformity of the locally-APN functions $F_{2,\pm 1}$, thereby disproving a conjecture proposed in \cite{budaghyan2024arithmetization} which states that there exist infinitely many $q$ and $u$ such that $F_{2,u}$ is an APN function.
Related papers
- On the second-order zero differential properties of several classes of power functions over finite fields [4.100056500795057]
Feistel Boomerang Connectivity Table (FBCT) is an important cryptanalytic technique on analysing the resistance of the Feistel network-based ciphers to power attacks such as differential and boomerang attacks.
In this paper, by computing the number of solutions of specific equations over finite fields, we determine explicitly the second-order zero differential spectra of power functions $x2m+3$ and $x2m+5$.
The computation of these entries and the cardinalities in each table aimed to facilitate the analysis of differential and boomerang cryptanalysis of S-boxes.
arXiv Detail & Related papers (2024-09-18T04:27:03Z) - Further Investigation on Differential Properties of the Generalized Ness-Helleseth Function [13.67029767623542]
The function defined by $f_u(x)=uxd_1+xd_2$ is called the generalized Ness-Helleseth function over $mathbbF_pn$.
For each $u$ satisfying $chi(u+1) = chi(u-1)$, the differential spectrum of $f_u(x)$ is investigated.
arXiv Detail & Related papers (2024-08-30T13:18:23Z) - On the differential and Walsh spectra of $x^{2q+1}$ over $\mathbb{F}_{q^2}$ [28.489574654566677]
We determine the differential spectrum of the power function $F(x)=x2q+1$ over $mathbbF_q2$.
When the characteristic of $mathbbF_q2$ is $3$, we also determine the value distribution of the Walsh spectrum of $F$.
arXiv Detail & Related papers (2024-07-08T14:01:06Z) - Quantum charges of harmonic oscillators [55.2480439325792]
We show that the energy eigenfunctions $psi_n$ with $nge 1$ are complex coordinates on orbifolds $mathbbR2/mathbbZ_n$.
We also discuss "antioscillators" with opposite quantum charges and the same positive energy.
arXiv Detail & Related papers (2024-04-02T09:16:18Z) - Provably learning a multi-head attention layer [55.2904547651831]
Multi-head attention layer is one of the key components of the transformer architecture that sets it apart from traditional feed-forward models.
In this work, we initiate the study of provably learning a multi-head attention layer from random examples.
We prove computational lower bounds showing that in the worst case, exponential dependence on $m$ is unavoidable.
arXiv Detail & Related papers (2024-02-06T15:39:09Z) - Dimension-free Remez Inequalities and norm designs [48.5897526636987]
A class of domains $X$ and test sets $Y$ -- termed emphnorm -- enjoy dimension-free Remez-type estimates.
We show that the supremum of $f$ does not increase by more than $mathcalO(log K)2d$ when $f$ is extended to the polytorus.
arXiv Detail & Related papers (2023-10-11T22:46:09Z) - A Unified Framework for Uniform Signal Recovery in Nonlinear Generative
Compressed Sensing [68.80803866919123]
Under nonlinear measurements, most prior results are non-uniform, i.e., they hold with high probability for a fixed $mathbfx*$ rather than for all $mathbfx*$ simultaneously.
Our framework accommodates GCS with 1-bit/uniformly quantized observations and single index models as canonical examples.
We also develop a concentration inequality that produces tighter bounds for product processes whose index sets have low metric entropy.
arXiv Detail & Related papers (2023-09-25T17:54:19Z) - Learning a Single Neuron with Adversarial Label Noise via Gradient
Descent [50.659479930171585]
We study a function of the form $mathbfxmapstosigma(mathbfwcdotmathbfx)$ for monotone activations.
The goal of the learner is to output a hypothesis vector $mathbfw$ that $F(mathbbw)=C, epsilon$ with high probability.
arXiv Detail & Related papers (2022-06-17T17:55:43Z) - Depth and Feature Learning are Provably Beneficial for Neural Network
Discriminators [3.04585143845864]
We show that deep GAN discriminators are able to distinguish distributions that shallow discriminators cannot distinguish.
This confirms that feature learning is beneficial for discriminators.
arXiv Detail & Related papers (2021-12-27T19:03:22Z) - Random matrices in service of ML footprint: ternary random features with
no performance loss [55.30329197651178]
We show that the eigenspectrum of $bf K$ is independent of the distribution of the i.i.d. entries of $bf w$.
We propose a novel random technique, called Ternary Random Feature (TRF)
The computation of the proposed random features requires no multiplication and a factor of $b$ less bits for storage compared to classical random features.
arXiv Detail & Related papers (2021-10-05T09:33:49Z) - A Canonical Transform for Strengthening the Local $L^p$-Type Universal
Approximation Property [4.18804572788063]
$Lp$-type universal approximation theorems guarantee that a given machine learning model class $mathscrFsubseteq C(mathbbRd,mathbbRD)$ is dense in $Lp_mu(mathbbRd,mathbbRD)$.
This paper proposes a generic solution to this approximation theoretic problem by introducing a canonical transformation which "upgrades $mathscrF$'s approximation property"
arXiv Detail & Related papers (2020-06-24T17:46:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.