We investigate the computational complexity of a simple one-dimensional origami problem. We are given a paper strip P of length n + 1 and fold it into unit length by creasing at unit intervals. Consequently, we have several paper layers at each crease in general. The number of paper layers at each crease is called the crease width at the crease. For a given mountain-valley assignment of P, in general, there are exponentially many ways of folding the paper into unit length consistent with the assignment. It is known that the problem of finding a way of folding P to minimize the maximum crease width of the folded state is NP-complete. In this study, we investigate a related paper-folding problem. For any given folded state of P, each crease has its mountain-valley assignment and crease-width assignment. Then, can we retrieve the folded state uniquely when only partial information about these assignments is given? We introduce this natural problem as the crease-retrieve problem, for which there are a number of variants depending on the information given about the assignments. In this paper, we show that some cases are polynomial-time solvable and that some cases are strongly NP-complete.

In this paper we first define (t, s)-completely independent spanning trees, which is a generalization of completely independent spanning trees. A set of t spanning trees of a graph is (t, s)-completely independent if, for any pair of vertices u and v, among the set of t paths from u to v in the t spanning trees, at least s ≤ t paths are internally disjoint. By (t, s)-completely independent spanning trees, one can ensure any pair of vertices can communicate each other even if at most s - 1 vertices break down. We prove that every maximal planar graph has a set of (3, 2)-completely independent spanning trees, every tri-connected planar graph has a set of (3, 2)-completely independent spanning trees, and every 3D grid graph has a set of (3, 2)-completely independent spanning trees. Also one can compute them in linear time.

A Hopfield network is a mathematical model of a spin glass system, a theory of stochastic physics. This binary model has been extended to many high-dimensional models. A persistent challenge in these models has been determining the activation function and stability conditions. In this paper, we propose an algebraic Hopfield network (AHN), which encompasses most extensions of classic Hopfield networks. In AHNs, weights act as operators on neuron outputs in weighted sum inputs. Here we provide the activation function and stability conditions for AHNs, offering a foundation for developing novel Hopfield network models.

Quantum signal processing (QSP), which is a general technique for construction of 1-qubit rotation operators, is a strong framework for quantum algorithm design. We consider a scenario of information-theoretically secure computation for QSP: n parties who have private angle parameters make the evaluator perform the QSP from the sum of their angles in a privacy-preserving manner through noninteractive communication. In this scenario, we construct a private simultaneous messages (PSM) protocol for QSP, named Private QSP (PQSP). As an application, we construct an efficient PSM protocol for symmetric Boolean functions with an evaluator of 1-qubit workspace by adapting PQSP to the 1-qubit quantum algorithm for symmetric Boolean functions of Maslov et al. [1]. We also show that the technique of PQSP works for the 1-qubit program for Boolean functions of Cosentino et al. [2]. We construct an efficient PSM protocol for Boolean functions computable by O(log n)-depth circuits with an evaluator of 1-qubit writable workspace from their 1-qubit program [3]. Such efficient PSM protocols with space-bounded classical evaluators have not been known so far.

Private information retrieval (PIR) allows a client to obtain records from a database without revealing the retrieved index to the server. In the single-server model, it has been known that (plain) PIR is vulnerable to selective failure attacks, where a (malicious) server intends to learn information of an index by getting a client’s decoded result. Recently, as one solution for this problem, Ben-David et al. (TCC 2022) proposed verifiable PIR (vPIR) that allows a client to verify that the queried database satisfies certain properties. However, the existing vPIR scheme is not practically efficient, especially when we consider the multi-query setting, where a client makes multiple queries for a server to retrieve some records either in parallel or in sequence. In this paper, we introduce a new formalization of multi-query vPIR and provide an efficient scheme based on authenticated PIR (APIR) and succinct non-interatctive arguments of knowledge (SNARKs). More precisely, thanks to the nice property of APIR, the communication cost of our multi-query vPIR scheme is 𝒪(n ⋅ |a| + |π|), where n is the number of queries, |a| is the APIR communication size, and |π| is the SNARK proof size. That is, the communication includes only one SNARK proof. In addition to this result, to show the effectiveness of our multi-query vPIR scheme in a real-world scenario, we present a practical application of vPIR on the online certificate status protocol (OCSP) and provide a comprehensive theoretical evaluation on our scheme in this scenario. Especially in the setting of our application, we observe that integrating SNARK proofs (for verifiability) does not significantly increase the communication cost.

Public key encryption with equality test, proposed by Yang et al. (CT-RSA 2010), allows anyone to check whether two ciphertexts of distinct public keys are encryptions of the same plaintext or not using trapdoors, and identity-based encryption with equality test (IBEET) is its identity-based variant. As a variant of IBEET, IBEET against insider attacks (IBEETIA) was proposed by Wu et al. (ACISP 2017), where a token is defined for each identity and is used for encryption. Lee et al. (ACISP 2018) and Duong et al. (ProvSec 2019) proposed IBEETIA schemes constructed by identity-based encryption (IBE) related complexity assumptions. Later, Emura and Takayasu (IEICE Transactions 2023) demonstrated that symmetric key encryption and pseudo-random permutations are sufficient to construct IBEETIA which is secure in the previous security definition. These results suggest us to explore a condition of IBEETIA that requires to employ IBE-related complexity assumptions. In this paper, we demonstrate a sufficient condition that IBEETIA implies IBE. We define one-wayness against chosen-plaintext/ciphertext attacks for the token generator (OW-TG-CPA/CCA) and for token holders (OW-TH-CPA/CCA), which were not considered in the previous security definition. We show that OW-TG-CPA secure IBEETIA with additional conditions implies OW-CPA secure IBE, and show that Lee et al. and Duong et al. schemes provide the OW-TG-CPA security. On the other hand, we propose a generic construction of OW-TH-CCA secure IBEETIA from public key encryption. Our results suggest a design principle to efficiently construct IBEETIA without employing IBE-related complexity assumptions.

Key-insulated encryption (KIE) is one of the countermeasures against the exposure of secret keys in public-key cryptography. In KIE, a user can update secret keys with a helper key to ensure that even if many secret keys, where each of them corresponds to each time period, are leaked, the security for other time periods is not compromised. However, KIE does not have resilience against the partial exposure of secret keys. Although there is public key encryption resilient to such partial exposure, unlike KIE, it cannot ensure security against the exposure of a whole secret key. In this paper, we introduce leakage-resilient key-insulated encryption (LR-KIE) that satisfies resilience against both partial and whole exposure of secret keys. We show three LR-KIE schemes from any leakage-resilient identity-based encryption scheme and/or any leakage-resilient secret sharing scheme.

Forward security is a fundamental requirement in searchable encryption, where a newly generated ciphertext is not allowed to be searched by previously generated trapdoors. However, forward security is somewhat overlooked in the public key encryption with keyword search (PEKS) context and there are few proposals, whereas forward security has been stated as a default security notion in the (dynamic) symmetric searchable encryption (SSE) context. In this paper, we propose a generic construction of forward secure public key authenticated encryption with keyword search (FS-PAEKS) from PAEKS. In addition to PAEKS, we employ 0/1 encodings proposed by Lin et al. (ACNS 2005). We also show that the Jiang et al.’s FS-PAEKS scheme (The Computer Journal 2023) does not provide forward security. Our generic construction is quite simple, and it can also be applied to construct forward secure public key encryption with keyword search (FS-PEKS). Our generic construction yields a comparably efficient FS-PEKS scheme compared to the previous scheme. Moreover, it eliminates the hierarchical structure (Abdalla et al. (JoC 2016)) or attribute-based feature (Zeng et al. (IEEE Transactions on Cloud Computing 2022)) of the previous generic constructions which is meaningful from a feasibility perspective.

Aggregate signatures without the bilinear map is a challenging and important problem in aspects of both practical and theoretical cryptology. In order to construct an aggregate signature which does not use the bilinear map, it is general to restrict some functionality of aggregate signatures or to employ strong cryptographic assumptions. The aggregate signature with the pre-communication (ASwPC) is one of the variants of aggregate signatures to achieve the security from a standard cryptographic assumption without the bilinear map. The ASwPC requires signers to interact with each other to share a temporary randomness before they determine their messages to be signed. After the pre-communication, each signer can start the signing process individually. An instantiation of ASwPC is given based on the discrete logarithm (DL) assumption, and its security is proven in the random oracle and the knowledge of secret key (KOSK) model via a loose security reduction. In this paper, we aim to construct a new ASwPC scheme whose security is proven via a tight security reduction. We employ the DDH assumption rather than the DL assumption. The combination of the property of the decisional assumption and that of the KOSK model enables us to apply the lossy key technique even in the case of ASwPC. Then we can prove the security of our scheme with a tight security reduction.

ChaCha is a stream cipher that has been adopted in TLS1.3 and is widely used around the world. Therefore, any vulnerability in ChaCha has a significant global impact, making the security analysis of its permutation a critical issue. Currently, no analysis has successfully extended beyond 8 rounds of ChaCha, and reducing the computational complexity for fewer rounds remains a challenge for future research. The primary methods of analyzing ChaCha include differential analysis, which examines the relationship between input and output differences; linear analysis, based on linear approximations; and Differential-Linear analysis, a combination of both approaches. The computational complexity of Differential-Linear analysis depends heavily on the linear bias. Therefore, we focus on increasing the linear bias and aim to reduce the computational complexity by deriving a linear approximation with a larger bias. To achieve this, we first reduce the number of linear rounds to 3 or 3.5 in order to increase the bias. Then, we derive the linear approximation between 4 or 4.25 and 7 rounds of ChaCha and identify the corresponding input and output differences. Next, to further increase the number of analysis rounds, we extend the linear approximation derived from 7-round ChaCha analysis. We analyze the 7.25-round ChaCha Permutation with computational complexity of 2^182.57 and 2^104.20. In addition we perform Differential-Linear analysis for 7.5-round ChaCha with computational complexity of 2^222.54 and 2^132.18. Although our analysis is a distinguisher, it can be extended to a key recovery attacks or differential analysis by considering final adition, which would have a significant on the overall security analysis of ChaCha.

Privacy preservation in the learning phase of machine learning poses considerable challenges. Two main approaches are commonly used to address these challenges: adding noise to machine learning model parameters to improve accuracy, and using noisy data during the learning process to enhance privacy. Recently, the Scalable Unified Privacy-preserving Machine Learning framework (SUPM) has emerged as a promising solution, effectively balancing privacy and accuracy by integrating privacy protection across the stages of dimension reduction, training, and testing. This paper introduces a novel method that optimizes privacy budget allocation by assigning budgets to various attributes based on their relevance to the target attribute. This approach improves accuracy while minimizing the reduction of relevant attributes. When incorporated into SUPM, our algorithm enhances both accuracy and privacy preservation. We evaluate its performance using logistic regression and support vector machines as the underlying machine learning models, demonstrating its effectiveness in retaining accuracy and maintaining attribute integrity. Additionally, we compare our approach with other uneven privacy budget allocation methods, such as Markov-kRR, confirming the superiority of our technique. We further examine the specific conditions under which our method proves particularly effective for certain datasets.

Dosun-Fuwari is one of Nikoli’s pencil puzzles. It is known that the generalized Dosun-Fuwari puzzle is NP-complete. Due to the inherent difficulty of the puzzle, solvers may often question whether a solution exists. Such questions highlight the need for a method that can verify the existence of a solution without revealing it, thereby preserving the puzzle’s challenge. In this paper, we propose a physical zero-knowledge proof protocol for the Dosun-Fuwari puzzle, which can be executed using 4mn + 2n cards. Here, m × n is the size of the instance of the puzzle.

This study presents a recording system utilizing an array of eight hyperdirectional microphones designed for personal three-dimensional (3D) sound field reproduction via wave front synthesis. The recording positions of the hyperdirectional microphones were identified through impulse response measurement, enabling microphone array construction. To evaluate the localization performance of the constructed microphone array, the impulse responses were measured, replay sounds were synthesized, and the localization experiment was performed. Results demonstrated that the developed recording system outperformed ambisonic microphone, a standard conventional 3D sound field recording , in localization accuracy.

The audio-integrated active noise control (AIANC) system is a special type of active noise control (ANC) system that aims to suppress noise while broadcasting audio. Although the nonlinear effect in ANC systems has been studied for many years, current research on AIANC systems still focuses on linear approaches, which cannot match the practical systems well and will lead to performance degradation. This paper proposes a new nonlinear AIANC method based on the psychoacoustic gated convolutional recurrent network (PGCRN). First, a new AIANC system model is developed based on the gated convolutional recurrent network (GCRN). Then a psychoacoustic model is introduced to the loss function of the GCRN to improve the audio quality. Finally, a new automatic gain control (AGC) method based on the MFCC cosine similarity and the XE-NLMS algorithm is proposed for the new AIANC system to improve audio quality under low signal-to-noise rate (SNR) conditions. Experimental results show that the proposed AIANC method outperforms several conventional methods in different noisy and nonlinear environments.

Neural interactions under optimal excitatory/inhibitory (E/I) balance are among the most crucial mechanisms for realizing cognitive functions. Among the phenomena supported by this mechanism, the duration of a phenomenon known as perceptual alternation exhibits two representative characteristics: nondeterminism and the long-tailed property at the level of a large neural population. However, even in a system consisting of a single pair of excitatory and inhibitory neurons, called chaos-chaos intermittency (CCI), a similar intermittent alternation of neural activity emerges, involving intermittent transitions between multiple isolated attractors. We hypothesized that the characteristics of CCI dynamics in local excitatory-inhibitory neural circuits can describe the nondeterminism and long-tailed properties observed at a broad hierarchical level. We evaluated the changes in nondeterminism and long-tailed properties under different E/I balance conditions to test this hypothesis. First, we validated the determinism of two types of dynamics: 1) transitions between attractors and 2) behavior within attractors. This evaluation was performed using iterated amplitude-adjusted Fourier transform and multi-scale entropy analysis. Next, we characterize the long-tailed properties of the alternations. These properties were evaluated while gradually shifting the parameters from attractor-merging bifurcation. These results indicate that while behavior within attractors demonstrate determinism across all conditions, transitions between attractors lose nondeterminism as the predominance of excitatory neuron increases. Furthermore, the duration histograms lose their long-tailed properties as excitatory neurons become dominant. Consequently, the disappearance of determinism and long-tailed properties co-occurs, and the coexistence of nondeterminism and long-tailed properties is realized within specific domains of the E/I balance. This discovery contributes to our understanding of the importance of an optimal E/I balance for maintaining the characteristics of interactions between excitatory and inhibitory neurons.

Global routing is one of the most crucial steps for design closure in the physical design of VLSI. This paper proposes a routing algorithm, called UEO algorithm, for generalized channels to achieve a small local congestion which is mainly dedicated to global routing for CMOS circuits designed to match 3D bonding technology. In our generalized channel formulation, due to tight global horizontal routing capacity, the connection of a net is restricted to a single-trunk Steiner tree. Routing algorithms proposed for the generalized channel so far achieve a small total vertical wire length while achieving the minimum number of used tracks, but they do not take a local vertical congestion into account, and the completion of detailed routing may suffer from a large local vertical congestion. The proposed UEO algorithm iteratively determines the assignment of trunks of nets based on the net priority proposed in this paper to achieve a small local vertical congestion. In experiments, it is confirmed that UEO achieves a small local vertical congestion, and that this work contributes to achieve design closure of routing design for 3D VLSI.

The integration of numerous IoT devices into the distribution network supports coordinated control of grid-connected devices, but the complex topology of distribution networks and uneven base station distribution result in weak coverage and uneven load, leading to poor end-to-end latency performance. While IoT heterogeneous integrated networking technologies enable low-latency access for many power devices, challenges like communication resource competition and slow optimization under uncertain network conditions remain. To address these issues, this paper proposes a joint optimization model for relay scheduling, data compression, and time scheduling, aiming to minimize average end-to-end latency. A two-stage edge-end cooperative resource optimization algorithm based on Lyapunov optimization theory is proposed. In the first stage, a relay scheduling algorithm using relay device connectivity and queue delay-aware ascending price matching optimizes scheduling by dynamically adjusting channel matching costs based on connectivity and queue backlogs. The second stage introduces a delay deviation-aware adaptive particle swarm optimization to optimize time scheduling and data compression, achieving fast convergence. The relay scheduling preferences are updated based on the final objective function value. Simulation results demonstrate the method’s effectiveness in reducing latency, improving network performance, and efficiently utilizing network resources.

Attribute-based credential (ABC) system allows a user to anonymously prove his/her attributes to a verifier, using a credential issued by an issuer. In the conventional ABC systems, since the verifier knows who is the issuer from the attribute proof, the information on the issuer can reveal some attributes of the user. Thus, an issuer-hiding ABC was recently proposed, where the issuer’s ID (and the issuer’s public key) is hidden. However, in the previous system, the verifier decides the accepted issuers and issues the signatures on the issuers’ public keys to each user in advance, and thus the computational and communication costs depending on the number of issuers are required whenever an issuer is revoked. In this paper, we propose an issuer-revocable issuer-hiding ABC system that is extended from the previous system. In the proposed system, the verifier generates and issues the signatures of the issuers’ public keys once. Then, whenever an issuer is revoked, the verifier sends only a short revocation list using a pairing-based accumulator to each user.

APN functions provide the optimal resistance to differential attacks. In 2022, Li et al. [IEEE TIT 68(7), 4761-4769 (2022)] constructed an infinite family of quadratic APN functions over 𝔽_2^2m with gcd(3, m) = 1 in the bivariate form F(x, y) = (x³ + xy² + y³ + xy, x⁵ + x⁴y + y⁵ + xy + x²y²). In this work, we theoretically prove that functions in a more general form F'(x, y) = (x^{2k + 1} + xy^2k + y^2k+1 + $\sum_{i=0}^{k-1}$ (xy)²ⁱ, x^{22k + 1} + x^22ky + y^{22k + 1} + $\sum_{i=0}^{k-1}$ (xy + (xy)^2k)²ⁱ) are CCZ-inequivalent to APN power functions on 𝔽_2^2m with gcd (3k, m) = 1.

Differential privacy is used to guarantee privacy protection and has become the de facto standard for privacy protection data analysis. The (α, ε)-Rényi differential privacy ((α, ε)-RDP), which is based on the Rényi divergence, has been proposed as a relaxation of the ε-differential privacy (ε-DP). The Rényi divergence is a generalization of the Kullback-Leibler divergence. The f-divergence, on the other hand, is also a generalization of the Kullback-Leibler divergence, where f : [0, ∞) → ℝ is a convex function satisfying f(1) = 0. Hence, we can consider differential privacy based on the f-divergence in the same manner as the Rényi differential privacy. This paper introduces (f, ε)-differential privacy ((f, ε)-DP) based on the f-divergence. We prove a novel composition theorem of an adaptive composition of n mechanisms all satisfying ε-DP. To derive this result, the following three propositions play an important role: (i) a probability preservation inequality via the f-divergence; (ii) a composition of two (f, ε)-DP; (iii) a relationship between the ε-DP and the (f, ε)-DP. Numerical examples show that there are cases where the proposed composition theorem is tighter than the previous composition theorems.

Sequences and combinatorics, particularly in coding theory, have seen increasing attention in recent years, especially in the study of difference systems of sets (DSSs) and partitioned difference families (PDFs) also known as zero-difference balanced (ZDB) functions. This paper has two main objectives: first, to propose a new, generic construction method for optimal DSSs using PDFs, and second, to introduce a direct method for constructing PDFs over cyclic groups. This direct construction, combined with a recursive approach, can produce desirable PDFs that may be used to generate DSSs leading comma-free codes with relatively high code rates. Some of the DSSs produced through this new construction are optimal when compared to known theoretical bounds.

Multi-attribute decision-making (MADM) is crucial in various fields. Most of MADM methods based on attribute values primarily handle scenarios where attribute information is expressed as an exact value. With the wide application of methods, the problem of uncertainty arises gradually, leading to attribute values that are not single values. The paper constructs a three-way decision (TWD) model based on probabilistic dominance relation (PDR) under interval-valued intuitionistic fuzzy number (IVIFN). This model is designed to address imprecise information by utilizing IVIFN. Meanwhile, we innovatively introduce attribute dominance degree to construct loss function matrix, which makes full use of interval number information. Moreover, we propose a novel method to calculate conditional probability under PDR, thus constructing a novel TWD model that assists decision makers in ranking and classifying. Finally, this paper demonstrates the reliability of the model in ranking through Spearman rank correlation coefficient (SRCC). Furthermore, the experimental result on UCI dataset shows that the model has great classification capabilities, with a 3.1% reduction in the average error rate.

We propose a zero-order-hold (ZOH) triggered prescribed-time controller for a chain of integrators. Our proposed controller derives the state into the arbitrarily small ball around the origin at the prescribed-time irrespective of the initial conditions while the control input is only updated discretely. We carry out the rigorous system analysis using Razumikhin theorem to prove the boundness of the state and control input. We give simulation results to illustrate the validity of our control method.

The Agile Earth Observation Satellite Constellation Mission Planning (AEOSCMP) problem focuses on optimizing target selection and scheduling for multiple satellites to maximize global observation rewards while adhering to resource constraints. To tackle this challenging task, this letter employs the Multi-Agent Transformer (MAT) to convert the joint policy search problem into a sequential decision-making process, optimizing observation policies through the attention mechanism. This approach could provide a theoretical guarantee of monotonic improvement during online training, ensuring consistent and reliable performance enhancements. Experimental results demonstrate that MAT achieves superior observation efficiency compared to state-of-the-art Multi-Agent Reinforcement Learning (MARL) methods.

A fully-stretching piecewise-linear (FSPL) chaotic map and a threshold function can generate a Markov binary sequence having exponentially vanishing auto-correlations. Based on the conditions of FSPL maps, we discuss bounds on the auto-correlations of the Markov binary sequences.

An effective method for virtualized network function forwarding graph (VNF-FG) placing based on graph attention network (GAT) and reinforcement learning (RL) is proposed for complex services and dynamic network conditions. We formulate a VNF-FG placing optimization problem, and designed a GAT-based RL agent to recognize graph structure and obtain placing policy. Experiments prove the method effectiveness.

In this work, we present an infinite family of quadratic APN functions in bivariate form, which extends one of the two families of APN functions constructed by Li et al. in [IEEE TIT 68(7), 4761-4769 (2022)]. We show that for n = 10 from our construction, we can obtain APN functions CCZ-inequivalent to those belonging to known infinite families of APN functions.

This letter introduces an efficient concatenation scheme using a novel discriminative mechanism for polar codes with Reed-Solomon (RS) codes. An efficient RS-polar soft information exchange process is proposed, employing the novel discriminative mechanism to reduce the number of RS codewords requiring decoding. Additionally, a low-complexity error and erasure decoding (EED) algorithm is utilized for RS codes in the scheme. These two measures collectively reduce overall complexity. Simulation results show that without compromising decoding performance, for RS-polar (8192,2732) scheme, at a signal to noise ratio of 2.0 dB, the proposed scheme reduces the average number of decoded RS codewords by 70.66%.

Cell-free massive MIMO systems serve users through geographically distributed access points (APs). However, if all APs provide services to each user, it will result in high power consumption and low energy efficiency. To solve this problem, we propose a user equipment (UE) priority criterion and an AP selection scheme. The criterion requires the system to prioritize serving more important UEs in order of priority. Moreover, a bidirectional restricted method was employed in the AP selection scheme. The outcomes of simulations indicate that the proposed scheme outperforms related works in both spectral efficiency and energy efficiency.

In this letter, we introduce a novel and straightforward moment generating function (MGF) expression of α-κ-μ fading channels by leveraging the generalized hypergeometric series. Moreover, by utilizing the derived MGF, we further present the bit error rate (BER) approximate expressions for various modulation schemes. Some numerical results are provided to verify the accuracy of our analytical expressions.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) can achieve full-space coverage of the signal compared to conventional reconfigurable intelligent surface (RIS). However, in existing works, the problem of energy efficiency optimization for multiple STAR-RISs-aided multi-user communication systems with the coupled phase-shift (CPS) constraint remains unsolved. To this end, this letter proposes a novel joint optimization framework for solving this problem. To overcome this complex nonlinear, nonconvex issue, we decompose the main problem into two individual subproblems: phase-shift optimization, and base station (BS) beamforming design. Penalty dual decomposition (PDD)-based and successive convex approximation (SCA) methods are employed to solve the two subproblems in the alternating optimization process. Simulation outcomes demonstrate that the proposed approach surpasses both centralized deployment strategies and traditional RIS-assisted systems.