The second-order zero differential spectra of some APN and other maps over finite fields
- URL: http://arxiv.org/abs/2310.13775v1
- Date: Fri, 20 Oct 2023 19:21:30 GMT
- Title: The second-order zero differential spectra of some APN and other maps over finite fields
- Authors: Kirpa Garg, Sartaj Ul Hasan, Constanza Riera, Pantelimon Stanica,
- Abstract summary: This paper presents a characterization of almost perfect nonlinear functions (APN) over fields of even characteristic in terms of second-order zero differential uniformity.
We show that our considered functions also have low second-order zero differential uniformity, though it may vary widely, unlike the case for even characteristic when it is always zero.
- Score: 5.537294943912028
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The Feistel Boomerang Connectivity Table and the related notion of $F$-Boomerang uniformity (also known as the second-order zero differential uniformity) has been recently introduced by Boukerrou et al.~\cite{Bouk}. These tools shall provide a major impetus in the analysis of the security of the Feistel network-based ciphers. In the same paper, a characterization of almost perfect nonlinear functions (APN) over fields of even characteristic in terms of second-order zero differential uniformity was also given. Here, we find a sufficient condition for an odd or even function over fields of odd characteristic to be an APN function, in terms of second-order zero differential uniformity. Moreover, we compute the second-order zero differential spectra of several APN or other low differential uniform functions, and show that our considered functions also have low second-order zero differential uniformity, though it may vary widely, unlike the case for even characteristic when it is always zero.
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) - Cubic power functions with optimal second-order differential uniformity [0.0]
We prove that $d=22k+2k+1$ and $gcd(k,n)=1$ have optimal second-order differential uniformity.
Up to affine equivalence, these might be the only optimal cubic power functions.
arXiv Detail & Related papers (2024-09-05T12:22:32Z) - The second-order zero differential uniformity of the swapped inverse functions over finite fields [2.2120851074630177]
We investigate the second-order zero differential uniformity of the swapped inverse functions.
This paper is the first result to characterize classes of non-power functions with the second-order zero differential uniformity equal to 4, in even characteristic.
arXiv Detail & Related papers (2024-05-27T03:11:57Z) - Some continuity properties of quantum R\'enyi divergences [0.0]
We prove the equality of two threshold values for the problem of binary quantum channel discrimination with product inputs.
Motivated by this, we give a detailed analysis of the continuity properties of various other quantum (channel) R'enyi divergences, which may be of independent interest.
arXiv Detail & Related papers (2022-09-01T17:59:14Z) - Unified Fourier-based Kernel and Nonlinearity Design for Equivariant
Networks on Homogeneous Spaces [52.424621227687894]
We introduce a unified framework for group equivariant networks on homogeneous spaces.
We take advantage of the sparsity of Fourier coefficients of the lifted feature fields.
We show that other methods treating features as the Fourier coefficients in the stabilizer subgroup are special cases of our activation.
arXiv Detail & Related papers (2022-06-16T17:59:01Z) - Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency [111.83670279016599]
We study reinforcement learning for partially observed decision processes (POMDPs) with infinite observation and state spaces.
We make the first attempt at partial observability and function approximation for a class of POMDPs with a linear structure.
arXiv Detail & Related papers (2022-04-20T21:15:38Z) - Factorized Fourier Neural Operators [77.47313102926017]
The Factorized Fourier Neural Operator (F-FNO) is a learning-based method for simulating partial differential equations.
We show that our model maintains an error rate of 2% while still running an order of magnitude faster than a numerical solver.
arXiv Detail & Related papers (2021-11-27T03:34:13Z) - Deep neural network approximation of analytic functions [91.3755431537592]
entropy bound for the spaces of neural networks with piecewise linear activation functions.
We derive an oracle inequality for the expected error of the considered penalized deep neural network estimators.
arXiv Detail & Related papers (2021-04-05T18:02:04Z) - Universal Approximation Property of Neural Ordinary Differential
Equations [19.861764482790544]
We show that NODEs can form an $Lp$-universal approximator for continuous maps under certain conditions.
We also show their stronger approximation property, namely the $sup$-universality for approximating a large class of diffeomorphisms.
arXiv Detail & Related papers (2020-12-04T05:53:21Z) - Finite-Function-Encoding Quantum States [52.77024349608834]
We introduce finite-function-encoding (FFE) states which encode arbitrary $d$-valued logic functions.
We investigate some of their structural properties.
arXiv Detail & Related papers (2020-12-01T13:53:23Z) - NP-PROV: Neural Processes with Position-Relevant-Only Variances [113.20013269514327]
We present a new member named Neural Processes with Position-Relevant-Only Variances (NP-PROV)
NP-PROV hypothesizes that a target point close to a context point has small uncertainty, regardless of the function value at that position.
Our evaluation on synthetic and real-world datasets reveals that NP-PROV can achieve state-of-the-art likelihood while retaining a bounded variance.
arXiv Detail & Related papers (2020-06-15T06:11:21Z)
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.