Search Results (400)

Background: Faced with the profound impact of major infectious diseases on public life and economic development, humans have long sought to understand disease transmission and intervention strategies. To better explore the impact of individuals’ different coping behaviors—triggered by changes in their psychological security due to public information and external environmental changes—on the spread to infectious diseases, the model will place greater emphasis on quantifying psychological factors to make it more aligned with real-world situations. Methods: To better understand the interplay between information dissemination and disease transmission, we propose a two-layer network model that incorporates psychological safety factors. Results: Our model reveals key insights into disease transmission dynamics: (1) active defense behaviors help reduce both disease spread and information diffusion; (2) passive resistance behaviors expand disease transmission and may trigger recurrence but enhance information spread; (3) high-timeliness, low-fuzziness information reduces the peak of the initial infection but does not significantly curb overall disease spread, and the rapid dissemination of disease-related information is most effective in limiting the early stages of transmission; and (4) community structures in information networks can effectively curb the spread of infectious diseases. Conclusions: These findings offer valuable theoretical support for public health strategies and disease prevention after government information release. Full article

Secure autonomous secret key distillation (SKD) systems traditionally depend on external common randomness (CR) sources, which often suffer from instability and limited reliability over long-term operation. In this work, we propose a novel SKD architecture that synthesizes CR by combining a keystream of a shared-key keystream generator $KSG\left(K_G\right)$ with locally generated binary Bernoulli noise. This construction emulates the statistical properties of the classical Maurer satellite scenario while enabling deterministic control over key parameters such as bit error rate, entropy, and leakage rate (LR). We derive a closed-form lower bound on the equivocation of the shared-secret key $K_G$ from the viewpoint of an adversary with access to public reconciliation data. This allows us to define an admissible operational region in which the system guarantees long-term secrecy through periodic key refreshes, without relying on advantage distillation. We integrate the Winnow protocol as the information reconciliation mechanism, optimized for short block lengths ($N=8$), and analyze its performance in terms of efficiency, LR, and final key disagreement rate (KDR). The proposed system operates in two modes: ideal secrecy, achieving secret key rates up to 22% under stringent constraints (KDR < 10⁻⁵, LR < 10⁻¹⁰), and perfect secrecy mode, which approximately halves the key rate. Notably, these security guarantees are achieved autonomously, without reliance on advantage distillation or external CR sources. Theoretical findings are further supported by experimental verification demonstrating the practical viability of the proposed system under realistic conditions. This study introduces, for the first time, an autonomous CR-based SKD system with provable security performance independent of communication channels or external randomness, thus enhancing the practical viability of secure key distribution schemes. Full article

The rapid development of network communication technology has led to an increased focus on the security of image storage and transmission in multimedia information. This paper proposes an enhanced image security communication scheme based on Fibonacci interleaved diffusion and non-degenerate chaotic system to address the inadequacy of current image encryption technology. The scheme utilizes a hash function to extract the hash characteristic values of the plaintext image, generating initial perturbation keys to drive the chaotic system to generate initial pseudo-random sequences. Subsequently, the input image is subjected to a light scrambling process at the bit level. The Q matrix generated by the Fibonacci sequence is then employed to diffuse the obtained intermediate cipher image. The final ciphertext image is then generated by random direction confusion. Throughout the encryption process, plaintext correlation mechanisms are employed. Consequently, due to the feedback loop of the plaintext, this algorithm is capable of resisting known-plaintext attacks and chosen-plaintext attacks. Theoretical analysis and empirical results demonstrate that the algorithm fulfils the cryptographic requirements of confusion, diffusion, and avalanche effects, while also exhibiting a robust password space and excellent numerical statistical properties. Consequently, the security enhancement mechanism based on Fibonacci interleaved diffusion and non-degenerate chaotic system proposed in this paper effectively enhances the algorithm’s resistance to cryptographic attacks. Full article

Quantum private comparison (QPC) is a quantum cryptographic protocol designed to enable two mutually distrustful parties to securely compare sensitive data without disclosing their private information to each other or any external entities. This study proposes a novel QPC protocol that leverages Bell states to ensure data privacy, utilizing the fundamental principles of quantum mechanics. Within this framework, two participants, each possessing a secret integer, encode the binary representation of their values using Pauli-X and Pauli-Z operators applied to quantum states transmitted from a semi-honest third party (TP). The TP, which is bound to protocol compliance and prohibited from colluding with either participant, measures the received sequences to determine the comparison result without accessing the participants’ original inputs. Theoretical analyses and simulations validate the protocol’s strong security, high efficiency, and practical feasibility in quantum computing environments. An advantage of the proposed protocol lies in its optimized utilization of Bell states, which enhances qubit efficiency and experimental practicality. Moreover, the proposed protocol outperforms several existing Bell-state-based QPC schemes in terms of efficiency. Full article

Currently, quantum steganography schemes utilizing the least significant bit (LSB) approach are primarily optimized for fixed-point data processing, yet they encounter precision limitations when handling extended floating-point data structures owing to quantization error accumulation. To overcome precision constraints in quantum data hiding, the EPlsb-MFQS and MVlsb-MFQS quantum steganography algorithms are constructed based on the LSB approach in this study. The multichannel floating-point quantum representation of digital signals (MFQS) model enhances information hiding by augmenting the number of available channels, thereby increasing the embedding capacity of the LSB approach. Firstly, we analyze the limitations of fixed-point signals steganography schemes and propose the conventional quantum steganography scheme based on the LSB approach for the MFQS model, achieving enhanced embedding capacity. Moreover, the enhanced embedding efficiency of the EPlsb-MFQS algorithm primarily stems from the superposition probability adjustment of the LSB approach. Then, to prevent an unauthorized person easily extracting secret messages, we utilize channel qubits and position qubits as novel carriers during quantum message encoding. The secret message is encoded into the signal’s qubits of the transmission using a particular modulo value rather than through sequential embedding, thereby enhancing the security and reducing the time complexity in the MVlsb-MFQS algorithm. However, this algorithm in the spatial domain has low robustness and security. Therefore, an improved method of transferring the steganographic process to the quantum Fourier transformed domain to further enhance security is also proposed. This scheme establishes the essential building blocks for quantum signal processing, paving the way for advanced quantum algorithms. Compared with available quantum steganography schemes, the proposed steganography schemes achieve significant improvements in embedding efficiency and security. Finally, we theoretically delineate, in detail, the quantum circuit design and operation process. Full article

The multi-factor authentication (MFA) protocol requires users to provide a combination of a password, a smart card and biometric data as verification factors to gain access to the services they need. In a single-server MFA system, users accessing multiple distinct servers must register separately for each server, manage multiple smart cards, and remember numerous passwords. In contrast, an MFA system designed for multi-server architecture allows users to register once at a registration center (RC) and then access all associated servers with a single smart card and one password. MFA with an offline RC addresses the computational bottleneck and single-point failure issues associated with the RC. In this paper, we propose a post-quantum secure MFA protocol for a multi-server architecture with an offline RC. Our MFA protocol utilizes the post-quantum secure Kyber key encapsulation mechanism and an information-theoretically secure fuzzy extractor as its building blocks. We formally prove the post-quantum semantic security of our MFA protocol under the real or random (ROR) model in the random oracle paradigm. Compared to related protocols, our protocol achieves higher efficiency and maintains reasonable communication overhead. Full article

Amid global efforts to balance sustainable development and food security, ecosystem service value (ESV), a critical bridge between natural systems and human well-being, has gained increasing importance. This study explores the spatiotemporal dynamics and driving factors of land use changes and ESV from a food security perspective, aiming to inform synergies between ecological protection and food production for regional sustainability. Using Guangdong Province as a case study, we analyze ESV patterns and spatial correlations from 2005 to 2023 based on three-phase land use and socioeconomic datasets. Key findings: I. Forestland and cropland dominate Guangdong’s land use, which is marked by the expansion of construction land and the shrinking of agricultural and forest areas. II. Overall ESV declined slightly: northern ecological zones remained stable, while eastern/western regions saw mild decreases, with cropland loss threatening grain self-sufficiency. III. Irrigation scale, forestry output, and fertilizer use exhibited strong interactive effects on ESV, whereas urban hierarchy influenced ESV independently. IV. ESV showed significant positive spatial autocorrelation, with stable agglomeration patterns across the province. The research provides policy insights for optimizing cropland protection and enhancing coordination between food production spaces and ecosystem services, while offering theoretical support for land use regulation and agricultural resilience in addressing regional food security challenges. Full article

Collegiate student-athletes are particularly vulnerable to food insecurity (FI). Prevalence rates range from 9.9% to 65%, although research is limited among this population. Background/Objectives: The challenge of balancing academic and degree progression requirements with training and competition demands can increase the risk for FI among student-athletes. Furthermore, insufficient funds for food has been reported for student-athletes living both on campus and off campus. Methods: This qualitative study employed a phenomenological design and constructivist theoretical framework to explore the experiences of athletic trainers, sports dietitians/nutritionists, and other professionals working with student-athletes in identifying and addressing FI among student-athletes via a series of online focus groups. Results: Participants (n = 27, 12 public colleges) had ≥7 years of collegiate athletics work experience, and most had been in their current position for <3 years. Five approaches to FI screening emerged; specifically, no screening, screening varies by team/sport, informal screening, dietitian screening, and formal screening. Emerging social determinants of FI included financial challenges, competing priorities, cultural/societal impacts, limited life skills, and the food environment. All these factors precipitated on a systems level, including individual, team/athletic department, and university/societal tiers. Conclusions: Athletic department and university policies and budgetary decisions emerged as potential antagonists of food security among student-athletes. FI mitigation strategies for student-athletes must go beyond simply addressing individual factors. Obtainment of food security among collegiate student-athletes requires system changes at the team/athletic department and university tiers. Full article

The rapid advancement and broad adoption of digital technologies have infused ESG practices with new dimensions and significance. Drawing on panel data from Chinese A-share listed companies spanning from 2012 to 2023, this paper aims to explain the impact of digital transformation on corporate ESG performance, explore its mechanisms and external regulatory effects, and provide systematic ideas and methods for improving corporate ESG performance from the perspective of digital transformation. The key findings of this study are summarized as follows: (1) Digital transformation (DT) has a significant positive effect on corporate ESG performance, and this association remains statistically robust following multiple robustness tests and a correction for potential endogeneity. (2) An analysis of the entire operational process reveals that DT improves ESG performance through enhancing environmental information disclosure quality, strengthening the integration of digital and physical industry technologies, and bolstering supply chain resilience. (3) The implementation of the “Broadband China” strategy exerts a positive moderating effect on the linkage between DT and ESG performance. (4) A heterogeneity analysis shows that the positive impact of DT on ESG performance is more significant and stable in non-state-owned enterprises, eastern regions, less-polluted areas, and growth stage enterprises. These findings offer theoretical and empirical insights for understanding ESG performance drivers. However, the focus on Chinese A-share firms and the use of Sino-Securities ratings may limit generalizability, warranting further improvement. Full article

As the Internet of Things (IoT) expands, robust tools for assessing privacy risk are increasingly critical. This research introduces a quantitative framework for evaluating IoT privacy risks, centered on two algorithmically derived scores: the Personalized Privacy Assistant (PPA) score and the PrivacyCheck score, both developed by the Center for Identity at The University of Texas. We analyze the correlation between these scores across multiple types of sensitive data—including email, social security numbers, and location—to understand their effectiveness in detecting privacy vulnerabilities. Our approach leverages Bayesian networks with cycle decomposition to capture complex dependencies among risk factors and applies entropy-based metrics to quantify informational uncertainty in privacy assessments. Experimental results highlight the strengths and limitations of each tool and demonstrate the value of combining data-driven risk scoring, information-theoretic analysis, and network modeling for privacy evaluation in IoT environments. Full article

To solve the fundamental problem of excessive consumption of classical resources and the simultaneous security vulnerabilities in semi-quantum dialogue systems, a multi-party controlled semi-quantum dialogue protocol based on hyperentangled Bell states is proposed. A single controlling party is vulnerable to information compromise due to tampering or betrayal; the multi-party controlled mechanism (Charlie₁ to Charlie_n) in this protocol establishes a distributed trust model. It mandates collective authorization from all controlling parties, significantly enhancing its robust resilience against untrustworthy controllers or collusion attacks. The classical participant Bob uses an adaptive Huffman compression algorithm to provide a framework for information transmission. This encoding mechanism assigns values to each character by constructing a Huffman tree, generating optimal prefix codes that significantly optimize the storage space complexity for the classical participant. By integrating the “immediate measurement and transmission” mechanism into the multi-party controlled semi-quantum dialogue protocol and coupling it with Huffman compression coding technology, this framework enables classical parties to execute encoding and decoding operations. The security of this protocol is rigorously proven through information-theoretic analysis and shows that it is resistant to common attacks. Furthermore, even in the presence of malicious controlling parties, this protocol robustly safeguards secret information against theft. The efficiency analysis shows that the proposed protocol provides benefits such as high communication efficiency and lower resource consumption for classical participants. Full article

As an important branch of secure multi-party computation, privacy set intersection enables multiple parties to input their private sets and jointly compute the intersection of these sets without revealing any information other than the intersection itself. With the increasing demand for privacy protection of user data, privacy set intersection has been widely used in privacy computing and other fields. In this paper, we utilize the properties of mutually unbiased bases to propose a multi-party quantum private set intersection protocol that incorporates identity authentication mechanisms. A semi-honest third party (TP) is introduced to facilitate the secure execution of this task among the multiple participating parties. The TP establishes a shared master key with each party, which serves as the basis for authenticating the identity of each participant throughout the protocol. Single-particle quantum states, prepared by the TP, act as the information carriers and are sequentially transmitted among the participating parties. Each party performs a local unitary operation on the circulating particle, thereby encoding their private data within the quantum state. At the end of the protocol, the TP announces his measurement result, by which all participants can concurrently ascertain the intersection of their private data sets. Notably, the proposed protocol eliminates the need for long-term storage of single-particle quantum states, thereby rendering it feasible with existing quantum technological capabilities. Furthermore, a comprehensive security analysis demonstrates that the protocol effectively resists some common external and internal attacks, thereby ensuring its theoretical security. Full article

Malware remains a central tool in cyberattacks, and systematic research into adversarial attack techniques targeting malware is crucial in advancing detection and defense systems that can evolve over time. Although numerous review articles already exist in this area, there is still a lack of comprehensive exploration into emerging artificial intelligence technologies such as reinforcement learning from the attacker’s perspective. To address this gap, we propose a foundational reinforcement learning (RL)-based framework for adversarial malware generation and develop a systematic evaluation methodology to dissect the internal mechanisms of generative models across multiple key dimensions, including action space design, state space representation, and reward function construction. Drawing from a comprehensive review and synthesis of the existing literature, we identify several core findings. (1) The scale of the action space directly affects the model training efficiency. Meanwhile, factors such as the action diversity, operation determinism, execution order, and modification ratio indirectly influence the quality of the generated adversarial samples. (2) Comprehensive and sensitive state feature representations can compensate for the information loss caused by binary feedback from real-world detection engines, thereby enhancing both the effectiveness and stability of attacks. (3) A multi-dimensional reward signal effectively mitigates the policy fragility associated with single-metric rewards, improving the agent’s adaptability in complex environments. (4) While the current RL frameworks applied to malware generation exhibit diverse architectures, they share a common core: the modeling of discrete action spaces and continuous state spaces. In addition, this work explores future research directions in the area of adversarial malware generation and outlines the open challenges and critical issues faced by defenders in responding to such threats. Our goal is to provide both a theoretical foundation and practical guidance for building more robust and adaptive security detection mechanisms. Full article

A homodyne detector, which is also a common element in current telecommunication, is a core component of continuous-variable quantum key distribution (CV-QKD) since it is considered the simplest setup for the distinguishing of coherent states with minimum error. However, the theoretical security of CV-QKD is based on the assumption that the responses of the homodyne detector are always linear with respect to the input, which is impossible in practice. In the real world, a homodyne detector has a finite linear domain, so the linearity assumption is broken when the input is too large. Regarding this security vulnerability, the eavesdropper Eve can perform the so-called homodyne detector-blinding attack by saturating the homodyne detector and then stealing key information without being detected by the legitimate users. In this paper, we propose a countermeasure for the homodyne detector-blinding attack by using an adjustable optical attenuator with a feedback structure. Specifically, we estimate the suitable attenuation value in the data processing of CV-QKD and feed it back to the adjustable optical attenuator before the detector in real time. Numerical simulation shows that the proposed countermeasure can effectively defend against homodyne detector-blinding attacks and ensure the security of the Gaussian-modulated coherent state protocol with finite-size effect. Full article

This research explores the relationship between Product Lifecycle Management (PLM) and Industry 4.0 (I4.0) technologies with Integrated Information Management Systems (IIMS) and the impact on the Market Performance (MP) of organisations. A survey was conducted with 106 company managers with experience ranging from the strategic to the operational level of IIMS practices. The data were analysed quantitatively through Exploratory Factorial Analysis (EFA), Confirmatory Factorial Analysis (CFA), and Structural Equation Modelling (SEM). The results indicated that integrating IIMS, PLM, and I4.0 is crucial to improving the effectiveness of organisational processes. However, its direct impacts on MP are more moderate. This shows the need for companies to fully integrate IIMS with PLM and I4.0 technologies, taking advantage of the synergies observed between IoT, Automation, and AI to improve operational efficiency and information security. As for practical and sustainability implications, the research discusses the importance of data optimisation and process management, mediating impacts and investment strategies, training and organisational culture, strategic planning, and the efficient and responsible use of resources. The originality of this work is highlighted by its approach, considering the research context broadly and uniquely. SEM made this approach possible, where the structural model is evaluated entirely, resulting in how the constructs behave based on how they are modelled. In addition, the research contributes to expanding theoretical knowledge and studying the practical applications of the results in business policies. Full article