Boolean Functions Research Articles

A Constructive Approach for Threshold Function Identification

Threshold Function (TF) is a subset of Boolean function that can be represented with a single linear threshold gate (LTG). In the research about threshold logic, the identification of TF is an important task that determines whether a given function is a TF or not. In this article, we propose a sufficient and necessary condition for a function being a TF. With the proposed sufficient and necessary condition, we devise a TF identification algorithm. The experimental results show that the proposed approach saves 80% CPU time for identifying all the 8-input NP-class TFs as compared with the state-of-the-art. Furthermore, the LTGs corresponding to the identified TFs obtained by the proposed approach have smaller weights and threshold values than the state-of-the-art.

On (simple) decision tree rank

In the decision tree computation model for Boolean functions, the depth corresponds to query complexity, and the size corresponds to storage space. The depth measure is the most well-studied one, and is known to be polynomially related to several non-computational complexity measures of functions such as certificate complexity. The size measure is also studied, but to a lesser extent. Another decision tree measure that has received very little attention is the minimal rank of the decision tree, first introduced by Ehrenfeucht and Haussler in 1989. This measure is closely related to the logarithm of the size, but is not polynomially related to depth, and hence it can reveal additional information about the complexity of a function. It is characterised by the value of a Prover-Delayer game first proposed by Pudlák and Impagliazzo in the context of tree-like resolution proofs.In this paper we study this measure further. We obtain an upper bound on depth in terms of rank and Fourier sparsity. We obtain upper and lower bounds on rank in terms of (variants of) certificate complexity. We also obtain upper and lower bounds on the rank for composed functions in terms of the depth of the outer function and the rank of the inner function. This allows us to easily recover known asympotical lower bounds on logarithm of the size for Iterated AND-OR and Iterated 3-bit Majority. We compute the rank exactly for several natural functions and use them to show that all the bounds we have obtained are tight. We also show that rank in the simple decision tree model can be used to bound query complexity, or depth, in the more general conjunctive decision tree model. Finally, we improve upon the known size lower bound for the Tribes function and conclude that in the size-rank relationship for decision trees, obtained by Ehrenfeucht and Haussler, the upper bound for Tribes is asymptotically tight.

Generalized Affine Equivalence Checking of Boolean Functions via Reachability Analysis

We consider the problem of checking the generalized affine equivalence of two given Boolean functions. This problem arises in various computer-aided design (CAD) and cryptographic applications, such as circuit synthesis and Reed-Muller codes. Based on the theory of affine group acting on the Boolean functions, we define the coefficient spaces and transition relations, and transform the checking problem into reachability analysis of finite state machines. Two methods are proposed to check the affine equivalence of Boolean functions using Binary Decision Diagrams (BDDs) and Property Directed Reachability (PDR), respectively. Both methods can check affine equivalence of bent and semi-bent functions, which state-of-the-art methods can hardly handle. Furthermore, existing methods only consider the case of affine equivalence, while our methods can handle the generalized affine equivalence of subspaces of Boolean functions. In the application of circuit synthesis, our methods can significantly reduce the size of the library compared to Boolean matching. To classify Boolean functions up to the generalized affine equivalence, we propose a method to obtain a complete classification based on BDDs. In the experiments, we have successfully applied our methods to some examples that can hardly be solved by using the previous methods, thus validating the effectiveness of our methods.

Low c-differential uniformity of the swapped inverse function in odd characteristic

The study of Boolean functions with low c-differential uniformity has become recently an important topic of research. However, in odd characteristic case, there are not many results on the (c-)differential uniformity of functions that are not power functions. In this paper, we investigate the c-differential uniformity of the swapped inverse functions in odd characteristic, and show that their c-differential uniformities are at most 6 except for some special case.

A New Characterization of 2-Resilient Rotation Symmetric Boolean Functions

A New Characterization of 2-Resilient Rotation Symmetric Boolean Functions

Constructions of Spectrally Null Constrained Complete Complementary Codes via the Graph of Extended Boolean Functions

Complete complementary codes (CCCs) have important applications in communication, radar, and information security. In modern communication and radar systems, certain spectrum is reserved or prohibited from transmission, which leads to the so-called spectrally null constrained (SNC) problem. Compared with vast works on conventional CCCs, relatively little is known about SNC-CCCs. One objective of this paper is to derive several constructions of CCCs with more flexible settings from the graph of extended Boolean functions. This generalizes earlier achievements on CCCs from recent literature. Another objective of this paper is to employ the graphs of extended Boolean functions and polynomial representation of sequences to construct SNC-CCCs.

Predictor-corrector models for lightweight massive machine-type communications in Industry 4.0

Future Industry 4.0 scenarios are characterized by seamless integration between computational and physical processes. To achieve this objective, dense platforms made of small sensing nodes and other resource constraint devices are ubiquitously deployed. All these devices have a limited number of computational resources, just enough to perform the simple operation they are in charge of. The remaining operations are delegated to powerful gateways that manage sensing nodes, but resources are never unlimited, and as more and more devices are deployed on Industry 4.0 platforms, gateways present more problems to handle massive machine-type communications. Although the problems are diverse, those related to security are especially critical. To enable sensing nodes to establish secure communications, several semiconductor companies are currently promoting a new generation of devices based on Physical Unclonable Functions, whose usage grows every year in many real industrial scenarios. Those hardware devices do not consume any computational resource but force the gateway to keep large key-value catalogues for each individual node. In this context, memory usage is not scalable and processing delays increase exponentially with each new node on the platform. In this paper, we address this challenge through predictor-corrector models, representing the key-value catalogues. Models are mathematically complex, but we argue that they consume less computational resources than current approaches. The lightweight models are based on complex functions managed as Laurent series, cubic spline interpolations, and Boolean functions also developed as series. Unknown parameters in these models are predicted, and eventually corrected to calculate the output value for each given key. The initial parameters are based on the Kane Yee formula. An experimental analysis and a performance evaluation are provided in the experimental section, showing that the proposed approach causes a significant reduction in the resource consumption.

A classification of S-boxes generated by orthogonal cellular automata

Most of the approaches published in the literature to construct S-boxes via Cellular Automata (CA) work by either iterating a finite CA for several time steps, or by a one-shot application of the global rule. The main characteristic that brings together these works is that they employ a single CA rule to define the vectorial Boolean function of the S-box. In this work, we explore a different direction for the design of S-boxes that leverages on Orthogonal CA (OCA), i.e. pairs of CA rules giving rise to orthogonal Latin squares. The motivation stands on the facts that an OCA pair already defines a bijective transformation, and moreover the orthogonality property of the resulting Latin squares ensures a minimum amount of diffusion. We exhaustively enumerate all S-boxes generated by OCA pairs of diameter 4≤d≤6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$4 \\le d \\le 6$$\\end{document}, and measure their nonlinearity. Interestingly, we observe that for d=4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d=4$$\\end{document} and d=5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d=5$$\\end{document} all S-boxes are linear, despite the underlying CA local rules being nonlinear. The smallest nonlinear S-boxes emerges for d=6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d=6$$\\end{document}, but their nonlinearity is still too low to be used in practice. Nonetheless, we unearth an interesting structure of linear OCA S-boxes, proving that their Linear Components Space is itself the image of a linear CA, or equivalently a polynomial code. We finally classify all linear OCA S-boxes in terms of their generator polynomials.

Noise sensitivity and stability of deep neural networks for binary classification

A first step is taken towards understanding often observed non-robustness phenomena of deep neural net (DNN) classifiers. This is done from the perspective of Boolean functions by asking if certain sequences of Boolean functions represented by common DNN models are noise sensitive or noise stable, concepts defined in the Boolean function literature. Due to the natural randomness in DNN models, these concepts are extended to annealed and quenched versions. Here we sort out the relation between these definitions and investigate the properties of two standard DNN architectures, the fully connected and convolutional models, when initiated with Gaussian weights.

Generalized partially bent functions, generalized perfect arrays, and cocyclic Butson matrices

In a recent survey, Schmidt compiled equivalences between generalized bent functions, group invariant Butson Hadamard matrices, and abelian splitting relative difference sets. We establish a broader network of equivalences by considering Butson matrices that are cocyclic rather than strictly group invariant. This result has several applications; for example, to the construction of Boolean functions whose expansions are generalized partially bent functions, including cases where no bent function can exist.

Short complete diagnostic tests for circuits with two additional inputs in some basis

Abstract We prove that any Boolean function in n variables can be modeled by a testable Boolean circuit with two additional inputs in the basis “conjunction, oblique conjunction, disjunction, negation” so that the circuit admits a complete diagnostic test of the length at most 2n + 3 with respect to stuck-at faults of the type 1 at gate outputs.

Synthesis of Modular Multipliers

The results of experiments on the circuit implementation of modular multipliers in the design library of ASIC (Application-Specific Integrated Circuits) and FPGA (Field-Programmable Gate Array) are presented. The initial descriptions of modular multiplier projects were given by systems of not fully defined (partial) Boolean functions and algorithmic VHDL descriptions. Logical optimization was carried out in the class of disjunctive normal forms (DNF) and representations of Boolean function systems by BDD (Binary Decision Diagrams). The synthesized circuits were evaluated by area and time delay. It is established that the use of partial Boolean function models and preliminary logi­cal BDD optimization allows one to improve the parameters of synthesized ASIC and FPGA blocks for small module values, however, the best solutions for large module values can be obtained using algorithmic VHDL descriptions of modular multipliers. In the synthesis of modular multiplier circuits as part of an FPGA and the use of ISE and Vivado design systems it is advisable to use synthesized VHDL operations (a*b) mod p.

Probabilistic estimation of the algebraic degree of Boolean functions

The algebraic degree is an important parameter of Boolean functions used in cryptography. When a function in a large number of variables is not given explicitly in algebraic normal form, it is usually not feasible to compute its degree, so we need to estimate it. We propose a probabilistic test for deciding whether the algebraic degree of a Boolean function f is below a certain value k. If the degree is indeed below k, then f will always pass the test, otherwise f will fail each instance of the test with a probability dtk(f)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{dt}_k(f)$$\\end{document}, which is closely related to the average number of monomials of degree k of the polynomials which are affine equivalent to f. The test has a good accuracy only if this probability dtk(f)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{dt}_k(f)$$\\end{document} of failing the test is not too small. We initiate the study of dtk(f)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{dt}_k(f)$$\\end{document} by showing that in the particular case when the degree of f is actually equal to k, the probability will be in the interval (0.288788, 0.5], and therefore a small number of runs of the test will be sufficient to give, with very high probability, the correct answer. Exact values of dtk(f)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{dt}_k(f)$$\\end{document} for all the polynomials in 8 variables were computed using the representatives listed by Hou and by Langevin and Leander.

Solving classification tasks by a receptron based on nonlinear optical speckle fields

Among several approaches to tackle the problem of energy consumption in modern computing systems, two solutions are currently investigated: one consists of artificial neural networks (ANNs) based on photonic technologies, the other is a different paradigm compared to ANNs and it is based on random networks of non-linear nanoscale junctions resulting from the assembling of nanoparticles or nanowires as substrates for neuromorphic computing. These networks show the presence of emergent complexity and collective phenomena in analogy with biological neural networks characterized by self-organization, redundancy, and non-linearity. Starting from this background, we propose and formalize a generalization of the perceptron model to describe a classification device based on a network of interacting units where the input weights are non-linearly dependent. We show that this model, called “receptron”, provides substantial advantages compared to the perceptron as, for example, the solution of non-linearly separable Boolean functions with a single device. The receptron model is used as a starting point for the implementation of an all-optical device that exploits the non-linearity of optical speckle fields produced by a solid scatterer. By encoding these speckle fields we generated a large variety of target Boolean functions. We demonstrate that by properly setting the model parameters, different classes of functions with different multiplicity can be solved efficiently. The optical implementation of the receptron scheme opens the way for the fabrication of a completely new class of optical devices for neuromorphic data processing based on a very simple hardware.

Estimation of small failure probabilities using the Accelerated Weight Histogram method

Simulation of rare events, such as small failure probabilities, is a common problem in several scientific and engineering fields. This paper presents a novel Monte-Carlo-based simulation approach for this purpose, called the Accelerated Weight Histogram (AWH) method. The method was originally developed to solve challenging sampling problems in statistical and biological physics, but its algorithm has here been reformulated for estimation of rare event probabilities. The applicability of the method is investigated for a couple of simpler computational examples and for a more advanced practical case, which consists of a rock tunnel stability problem. To estimate the probability of failure of the latter in a realistic manner, a boolean indicator function was used to describe failure, based on a mechanical concept known as unbalanced force ratio. The investigated cases indicate that the AWH method performs well for both simpler limit states and more complex failure definitions.

Code-routing: a new attack on position verification

The cryptographic task of position verification attempts to verify one party's location in spacetime by exploiting constraints on quantum information and relativistic causality. A popular verification scheme known as f-routing involves requiring the prover to redirect a quantum system based on the value of a Boolean function f. Cheating strategies for the f-routing scheme require the prover use pre-shared entanglement, and security of the scheme rests on assumptions about how much entanglement a prover can manipulate. Here, we give a new cheating strategy in which the quantum system is encoded into a secret-sharing scheme, and the authorization structure of the secret-sharing scheme is exploited to direct the system appropriately. This strategy completes the f-routing task using O(SPp(f)) EPR pairs, where SPp(f) is the minimal size of a span program over the field Zp computing f. This shows we can efficiently attack f-routing schemes whenever f is in the complexity class ModpL, after allowing for local pre-processing. The best earlier construction achieved the class L, which is believed to be strictly inside of ModpL. We also show that the size of a quantum secret sharing scheme with indicator function fI upper bounds entanglement cost of f-routing on the function fI.

Genetic Approach to Improve Cryptographic Properties of Balanced Boolean Functions Using Bent Functions

Recently, balanced Boolean functions with an even number n of variables achieving very good autocorrelation properties have been obtained for 12≤n≤26. These functions attain the maximum absolute value in the autocorrelation spectra (without considering the zero point) less than 2n2 and are found by using a heuristic search algorithm that is based on the design method of an infinite class of such functions for a higher number of variables. Here, we consider balanced Boolean functions that are closest to the bent functions in terms of the Hamming distance and perform a genetic algorithm efficiently aiming to optimize their cryptographic properties, which provides better absolute indicator values for all of those values of n for the first time. We also observe that among our results, the functions for 16≤n≤26 have nonlinearity greater than 2n−1−2n2. In the process, our search strategy produces balanced Boolean functions with the best-known nonlinearity for 8≤n≤16.

On the Gowers [formula omitted] and [formula omitted] norms of Boolean functions and their restriction to hyperplanes

It is known that the smaller the values of Gowers U2 and U3 norms of a Boolean function, the greater its strength against linear and quadratic approximations. This article gives recursive relation between the Gowers U2 and U3 norms of Boolean functions and their restriction to hyperplanes. These results will help analyze the second and third-order Gowers norms of Boolean functions in a higher number of variables.

A unique universal block in QCA technology

The limitations of Complementary Metal Oxide Semiconductor (CMOS) such as high-power dissipation, short channel effects, and high lithography has prompted scientists to look forward to alternatives. Many nanotechnologies were emerged and still in the competition such as Fin Field-Effect Transistor (FinFET), Carbon Nanotube Field-Effect Transistor (CNFET), Quantum-dot Cellular Automata (QCA). QCA is a new nanoelectronics technology used a new method for calculations and it is a promising technology in terms of power dissipation. In QCA, circuit complexity is depending on cell count, area and latency. For commercial purposes a universal gate is necessary, this gate can be used alone to design any Boolean function, where in CMOS technology, the NAND or NOT gates have the ability to be a universal gate. The dominant gates in QCA are the majority gate with inverter where QCA circuits cannot be designed using only a universal gate. Because the structure of the gate is important in QCA, this paper introduces a new low complexity singular block that can be used as a universal block. The proposed block can be used to form AND, OR, NOT, Exclusive-OR (XOR) and Multiplexer (MUX). So, this block not just used to design any circuit but also it can be used to form other important functions XOR and MUX to build large circuits in an optimum form.

ADRA: Extending Digital Computing-In-Memory With Asymmetric Dual-Row-Activation

Computing in-memory (CiM) has emerged as an attractive technique to mitigate the von-Neumann bottleneck. Current digital CiM approaches for in-memory operands are based on multi-wordline assertion for computing bit-wise Boolean functions and arithmetic functions such as addition. However, most of these techniques, due to the many-to-one mapping of input vectors to bitline voltages, are limited to CiM of commutative functions, leaving out an important class of computations such as subtraction. In this paper, we propose a CiM approach, which solves the mapping problem through an asymmetric wordline biasing scheme, enabling (a) simultaneous single-cycle memory read and CiM of primitive Boolean functions (b) computation of any Boolean function and (c) CiM of non-commutative functions such as subtraction and comparison. While the proposed technique is technology-agnostic, we show its utility for ferroelectric transistor (FeFET)-based non-volatile memory. Compared to the standard near-memory methods (which require two full memory accesses per operation), we show that our method can achieve a full scale two-operand digital CiM using just one memory access, leading to a 23.2% -72.6% decrease in energy-delay product (EDP).