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51,074 results · page 190 of 2,129
Arbee Mae L. Castro, Chanelie B. Tabliga, Patricia Antonette Merecido, Anthony Vince P. Bongo · 5 authors
No abstract is available for this record.
Xiuyuan Zhao, Jingyi Liu, Ying Wang, Jiyuan Wang
Cryptocurrency markets exhibit complex microstructural dynamics characterized by high-frequency volatility bursts, rapid regime switching, and long-range temporal dependencies, which expose several limitations of existing volatility forecasting approaches. In particular, attention-based models suffer from prohibitive quadratic computational cost on long high-frequency sequences, while many recurrent architectures struggle to adapt to regime transitions, asymmetric volatility responses, and risk-aware uncertainty estimation. To address these gaps, this paper proposesCryptoMamba-SSM, a novel volatility prediction framework built upon Mamba-based state space models with linear computational complexity. CryptoMamba-SSM integrates selective memory mechanisms with structured state space representations to effectively capture critical market microstructure signals arising from liquidity shocks and sentiment transitions, while dynamically adjusting memory retention across different volatility regimes. This design enables efficient modeling of long-sequence dependencies inherent in cryptocurrency price movements without incurring the computational bottlenecks of traditional attention-based architectures. Through comprehensive experiments on Bitcoin historical data spanning multiple market regimes, we demonstrate that CryptoMamba-SSM consistently outperforms conventional LSTM, GRU, and Transformer baselines, achieving up to a 23.7% reduction in Mean Absolute Error and a 31.2% improvement in directional accuracy. The selective memory mechanism effectively captures regime-switching behaviors and microstructural anomalies, leading to more reliable short-term volatility risk quantification. Moreover, the linear-time complexity of CryptoMamba-SSM enables real-time processing of high-frequency trading data while maintaining strong generalization across diverse market conditions.
Steven Paul Nohr
<b><i>Zero-knowledge proof (ZKP) systems</i></b> such as<b><i> zk-SNARKs</i></b> and<b><i> zk-STARKs</i></b> are increasingly promoted as comprehensive solutions for privacy, scalability, and selective disclosure in blockchain-based systems. While these cryptographic primitives provide strong guarantees regarding computational correctness and data confidentiality, they are frequently mischaracterized as substitutes for regulatory compliance, legal enforceability, or supervisory control mechanisms. This paper presents a structural and functional comparison between zero-knowledge proof systems and the Crystal Validator™ (CV), a logic-layer enforcement architecture designed to encode and enforce jurisdiction?aware regulatory requirements. We demonstrate that cryptographic validity proofs are orthogonal to—and insufficient for—legal compliance, accountability, and regulatory supervision as required under frameworks such as the EU Markets in Crypto-Assets Regulation (MiCA). We argue that regulatory enforcement logic must exist above cryptographic proof layers and cannot be replaced by them without introducing systemic compliance risk.
Hong Min, Yousef Ibrahim Daradkeh, Jung Taek Seo, Mohd Anjum · 5 authors
This study presents a computational modeling framework for efficient and secure computation offloading in Internet of Things (IoT)-enabled smart contract systems. The integration of IoT, edge computing, and blockchain introduces significant challenges, including limited device capacity, high verification cost, and scalability constraints. Existing blockchain verification approaches depend on computationally intensive cryptographic operations that are inefficient for resource-constrained IoT devices, resulting in increased latency, energy consumption, and transaction costs. To address these issues, this study proposes the Zero-Knowledge Fuzzy Logic Offloading and Rollup (Z-FLOR) framework, an adaptive and energy-efficient model designed to enable secure and verifiable computation in IoT-based smart contract systems. The proposed framework integrates three key components. First, a zero-knowledge proof-based verification model using the Groth16 zkSNARK module generates compact and privacy-preserving proofs that enable fast and reliable verification. Second, a Fuzzy Logic–Driven Energy-Aware Offloading module dynamically allocates computational tasks between IoT devices, edge servers, and cloud platforms based on energy availability, network delay, and device reliability. Third, an Optimistic Rollup Verification module aggregates proofs off-chain and submits them in batches to reduce gas costs and enhance scalability. Extensive simulation and experimental evaluation across diverse IoT scenarios demonstrate the effectiveness of the proposed computational framework. Results indicate that Z-FLOR achieves 99.7% verification accuracy and 98.9% proof compression efficiency, while gas cost analysis indicates gas cost reductions in the range of 80%–98%. Z-FLOR additionally achieves a 44.0% reduction in latency, 51.0% savings in gas costs, and 38.0% energy consumption compared to baseline approaches. These findings highlight the capability of the proposed approach to serve as a scalable and energy-efficient modeling solution for secure IoT smart contract execution in decentralized environments.
Nai-Hui Chia, Kai-Min Chung, Xiao Liang, Jiahui Liu
No abstract is available for this record.
Lukas Aumayr, Zeta Avarikioti, Matteo Maffei, Giulia Scaffino · 5 authors
No abstract is available for this record.
Sayan Bairagi
No abstract is available for this record.
Mohammad Muavia
No abstract is available for this record.
Prateek Sharma
No abstract is available for this record.
Ezekiel Ologunde
Modern vehicles are distributed embedded computing platforms whose expanding network connectivity-CAN bus, Bluetooth, cellular telematics, and over-the-air (OTA) update channels-exposes them to the same class of adversarial attacks studied in cloud and enterprise environments. Machine learning (ML)-based intrusion detection systems (IDS) have emerged as the primary defensive response, yet these models are themselves vulnerable to adversarial perturbation: a well-crafted malicious CAN frame can evade an ML-based IDS in the same way that an adversarial image patch fools a computer-vision classifier. This paper traces the threat landscape from foundational automotive attack-surface studies through contemporary adversarial ML research, examines how resource-constrained embedded platforms limit defensive options, and proposes a defense architecture that combines behavioral anomaly detection with zero-knowledge proof (ZKP) attestation for invehicle control units. We argue that ZKP-based component attestation-previously dismissed as computationally impractical for embedded systems-is now feasible given recent advances in succinct non-interactive arguments of knowledge (SNARKs), and that combining it with adversarially trained ML-IDS models yields defensein-depth that addresses both network-layer and hardware-layer attack vectors.
Fredrick Mito Ogodo
Entrepreneurship, innovation, and startup ecosystems have become central components of national development strategies, particularly in Sub-Saharan Africa, where youth unemployment, income inequality, and limited formal employment opportunities remain persistent structural challenges. This paper presents a systematic review of peer-reviewed studies published between 2020 and 2025 to examine how these interconnected elements contribute to Kenya’s socioeconomic development. Guided by the entrepreneurial passion theory and the risk-bearing theory of entrepreneurship, the review synthesizes both empirical and conceptual evidence across four thematic areas: job creation and poverty reduction; financing constraints and governance weaknesses; the role and reach of innovation hubs; and human capital and skills development. The findings indicate that entrepreneurship plays a significant role in employment generation, income creation, and technological progress in Kenya. Small and medium enterprises continue to absorb a substantial share of the labour force, particularly among youth. However, the study finds that the sector’s overall contribution to national development is limited by restricted access to affordable finance, inconsistent policy implementation, weak institutional coordination, and notable skill gaps among enterprise founders. These structural challenges reduce business survival rates and limit long-term growth. The review further finds that innovation hubs, including Nairobi’s iHub and university-based incubation centres, have created valuable support structures through mentorship, networking, and access to digital infrastructure. Despite these gains, their impact remains geographically concentrated and does not adequately address the needs of entrepreneurs operating outside major urban centres. Moreover, many programs do not sufficiently respond to practical business management and financing challenges faced by early-stage enterprises. The paper concludes that achieving Kenya’s Vision 2030 development objectives requires a coordinated and sustained strategy. The study therefore recommended that the government should strengthen entrepreneurship education, expand access to blended financing, decentralize innovation infrastructure, and improve institutional coordination to promote sustainable enterprise development in Kenya
Manju Bhardwaj, Shweta Sankhwar, Ojasvi Yadav, Rinkal Bhadauria · 6 authors
Over the past decade, decentralized digital currencies have gained prominence in finance and technology, but their growth has also drawn adversaries exploiting security vulnerabilities. This paper reviews the literature on cryptocurrency and security using bibliometric analysis of Web of Science and Scopus articles published between 2013 and May 2025. Tools such as Biblioshiny and VOSviewer were employed to explore key trends, influential contributors, collaborative networks, and emerging themes. A novel contribution of this study is the use of Large Language Models (LLMs) to address inconsistent affiliation formats, enabling accurate identification of leading academic organizations. The results demonstrate that LLM-based harmonization effectively prevents misrepresentation in bibliometric datasets. Overall, this study not only summarizes evolving research trends in cryptocurrency and security but also highlights the potential of LLMs to enhance bibliometric methods, suggesting broader applications for improving the accuracy and reliability of future scholarly analyses.
Jin Zhou, Hongzhi Lu, Jianxin Xiong
The integration of continuous Zero Trust Architecture (ZTA) into Segment Routing over IPv6 (SRv6) networks introduces severe performance bottlenecks and physical constraints of the Maximum Transmission Unit (MTU). Specifically, naively embedding massive Zero-Knowledge Proof (ZKP) for per-packet authentication inevitably triggers catastrophic fragmentation and disrupts stateless forwarding. To address these fundamental limitations, this paper proposes a novel session-bound zero-knowledge authorization framework tailored for SRv6 programmable data planes. Our architecture explicitly decouples heavyweight cryptographic validations from the active forwarding path. Massive ZKP payloads are processed asynchronously via payload transmission in the control plane, while the data plane enforces line-rate access control using lightweight 32-byte capability tokens encapsulated in customized SRv6 extension headers. Furthermore, to mathematically balance robust security with forwarding efficiency, we formulate the dynamic verification process as a risk-aware Partially Observable Markov Decision Process (POMDP). Using in-band network telemetry, we derive an Adaptive Threshold Verification (ATV) algorithm that yields a closed-form <inline-formula> <tex-math notation="LaTeX">$O(1)$ </tex-math></inline-formula> complexity optimal scheduling policy. Extensive evaluations demonstrate that the decoupled mechanism seamlessly resolves the MTU bottleneck, maintaining stable baseline throughput under massive concurrent sessions. Concurrently, the ATV algorithm intelligently adapts to real-time threat intensities, conserving control-plane resources during safe periods while instantaneously triggering precise re-verifications against covert and volumetric cyberattacks.
Y.Y.N. Li
We introduce behavior-bound signatures (BBS), a signature framework in which each signature attests not only to signer authenticity but also to the satisfaction of a prescribed behavioral policy. Unlike traditional digital signatures—whose acceptance is determined by identity validity alone—BBS enforces compliance at the level of the verification predicate: a signature is accepted if and only if a zero-knowledge proof establishes that a residual function value δ(x)=∣ϕ(x)−τ∣\delta(x)=|\phi(x)-\tau|δ(x)=∣ϕ(x)−τ∣ lies below a threshold ε\varepsilonε. Thus, compliance safety is reduced to zero-knowledge soundness rather than to external monitoring or honest-majority assumptions. We formalize policy-soundness under chosen-message attacks (PS-CMA), extending EUF-CMA by requiring that no adversary can produce a valid signature for any message whose induced action violates the policy predicate. We prove that BBS achieves PS-CMA security under standard assumptions: binding of Pedersen commitments, collision resistance of Poseidon, and soundness of the underlying zero-knowledge proof system (e.g., Bulletproofs or PLONK). Our construction instantiates the policy predicate via a private structure function ϕ(x)\phi(x)ϕ(x) and enforces δ(x)<ε\delta(x)<\varepsilonδ(x)<ε through a zero-knowledge range constraint, while revealing no information about the private parameters. Sequential signatures compose into a hash-linked trajectory, enabling verifiable ordering and completeness of action sequences. We additionally define the Function Approximation Inversion Problem (FAIP) as a conjectured hardness property of the structure function, and emphasize that the security of BBS does not rely on this conjecture.
Danae Maniatis, Kathryn J. Jeffery
Abstract The Congo Basin, comprising the world’s second-largest tropical rainforest, presents both critical environmental challenges and unique opportunities for sustainable development. This chapter evaluates key pathways for environmentally sustainable development in the region, with an emphasis on extractive industries, renewable energy, agroforestry, biodiversity conservation, ecotourism, and climate and carbon finance. Using regional indicators such as the Fragile States Index (FSI), Human Development Index (HDI), and Environmental Performance Index (EPI), the authors highlight the structural barriers—including weak governance, institutional fragility, and extreme poverty—that constrain the region’s development trajectory. Despite these challenges, the Basin’s ecological wealth offers potential for transformative interventions. Strategies such as Reduced-Impact Logging for Climate (RIL-C), sustainable mining practices, decentralized renewable energy systems, and integrated agroforestry models are analyzed for their capacity to reduce emissions, protect biodiversity, and enhance local livelihoods. The chapter further explores the potential of REDD+ and emerging carbon market frameworks to finance conservation and climate mitigation efforts. Emphasizing the role of participatory governance, indigenous knowledge systems, and scientific innovation, the chapter underscores the necessity of context-specific, cross-sectoral approaches to operationalize sustainability in one of the planet’s most ecologically and geopolitically complex regions.
Yuki Sawai, Kyoichi Asano, Yohei Watanabe, Mitsugu Iwamoto
Range arguments are a type of zero-knowledge proofs that aim to prove that a prover's committed value falls within a specified range for a verifier. Previously, most range arguments were constructed based on the discrete logarithm (DLOG) assumption, and hence, exponentiation operation is required for proof generation and verification. In addition, it is generally known that splitting a zero-knowledge proof protocol into a preprocessing phase and an online phase makes computation after fixing the input efficient. Still, such protocol has yet to be known for range arguments. This paper proposes an efficient range arguments protocol with a preprocessing phase. Our proposal takes a new approach by using arithmetic circuits to express the constraints that the prover must prove. The prover (resp. verifier) can generate (resp. verify) a part of proof based on multiplication and addition operations instead of exponentiation operations. Our range argument is a generic construction that does not rely on any particular mathematical assumptions, which enables us to construct a post-quantum range argument. The implementation evaluation shows that the total computation time for the prover and verifier in the online phase is efficient compared to Bulletproofs, one of the state-of-the-art range proofs. Especially, the prover computation is efficient.
Rongjun Chen, Yun Sun, Feng Xue, Yongzhi Ma · 8 authors
Addressing the challenges of Traditional Chinese Medicine (TCM) traceability systems, including heavy data storage burdens, poor privacy protection, and susceptibility to tampering, this study establishes a highly secure and trustworthy traceability supervision system for the entire Chinese medicine supply chain, which enhances product quality and safety assurance. Centred on the Hyperledger Fabric consortium blockchain as its core architecture, a multi-chain integration framework comprising one regulatory main chain plus five organisational sub-chains is proposed to achieve permission control, data isolation, and privacy. A multi-mode encrypted data storage mechanism is designed, integrating China’s national cryptographic algorithms SM4 and SM3 with CP-ABE attribute-based encryption to enable tiered management of private and non-private data. Zero-knowledge proof technology safeguards identity privacy during cross-chain data transmission, while QR codes and environmental data collection mechanisms enhance data entry efficiency and authenticity. The system achieves end-to-end traceability from cultivation and processing through transportation, warehousing, and sales. Comparative performance analysis shows that the proposed framework effectively alleviates data storage pressure, ensures data validity, enhances data security, and improves collaborative efficiency among organizations across the TCM supply chain. The proposed multi-chain integrated Chinese medicine traceability and supervision system enables efficient collaboration and trustworthy traceability across the entire Chinese medicine industry chain, while safeguarding data security and privacy, and has significant application and promotion value. Future integration with artificial intelligence and big data technologies could further enhance the system’s intelligent analysis and decision-support capabilities.
Behrang Forghani, David Robinson
We present an elementary proof that the asymptotic entropy of a random walk on a countable abelian group is zero when the entropy of the first step of the random walk is finite. Unlike the traditional proof, our approach does not rely on the boundary theory of random walks. To our best knowledge, our direct proof is new even for the group of integers.
Sophia Shim, Caleb Lee
We introduce the ω-Protocol, a zero-knowledge proof framework for the verification of elliptic curve–based homomorphic digital signatures. The protocol is constructed on top of the Elliptic Curve Homomorphic Digital Signature Algorithm (EHDSA) and enables zero-knowledge verification of signature validity while preserving signer privacy. The core contribution of the ω-Protocol is a signature-integrated zero-knowledge construction that combines homomorphic properties of EHDSA with algebraic commitment mechanisms over elliptic curve groups. We formalize the protocol model and define security notions capturing zero-knowledge, soundness, and unlinkability of signature verification. Under standard cryptographic assumptions over elliptic curve groups, we prove that the ω-Protocol achieves zero-knowledge and unforgeability-preserving verification without revealing signature components or ephemeral key material. We further analyze the computational complexity of the protocol and show that it incurs only minimal overhead compared to standard EHDSA verification. Our results establish a principled cryptographic framework for zero-knowledge verification of homomorphic digital signatures and provide a foundation applicable to privacy-preserving authentication and verification protocols.
Adaobi Ndukaji
No abstract is available for this record.
Damien Cubizol
No abstract is available for this record.
Anthony Chidi Nzomiwu, Francisca Uzooyibo Okoye, Benedict Iyke Okoronkwo
No abstract is available for this record.
Tomiwa Sunday Adebayo, Dervis Kirikkaleli
This study assesses whether Bitcoin’s linkage with AI equities remains robust after accounting for equity risk sentiment. To this end, the study employs the multiscale quantile‐on‐quantile correlation (MSQQC) and multiscale quantile‐on‐quantile partial correlation (MSQQPC) approaches, using daily data covering 02/01/2019–16/06/2025. The results indicate that BTC–AI comovement is strongly state‐ and frequency‐dependent rather than stable across the joint distribution or across horizons. In the high‐frequency band, dependence is weak and only intermittently significant, with localised negative regions around BTC ≈ 0.20 with AI ≈ 0.30–0.50 and BTC ≈ 0.30 with AI ≈ 0.70. In the mid‐frequency band, significance concentrates in the tails, showing negative dependence under downside stress conditions such as BTC ≈ 0.10–0.30 with AI ≈ 0.10, alongside sign changes when BTC is in upper‐tail states. In the low‐frequency band, dependence becomes broadly positive and significant across most quantile combinations, with limited decoupling when AI is highly elevated (≈ 0.80–0.90) and BTC is also in upper quantiles (≈ 0.70–0.90). Importantly, conditioning on VIX and VVIX does not materially alter these patterns, suggesting that sentiment influences segments of short‐run dependence but does not overturn the longer‐run BTC–AI linkage. The study derives policy recommendations from these findings.
Raniyah Wazirali, Fatma Foad Ashrif, Rami Ahmad
The rapid adoption of smart-home and Internet-of-Things (IoT) devices has intensified the need for privacy-preserving biometric authentication that is both secure and computationally efficient. This paper presents Hybrid-HE LLE, a practical framework that combines Locally Linear Embedding (LLE) with selective homomorphic encryption to protect face-recognition features in resource-constrained IoT environments. Unlike cloud-centric outsourcing, the proposed system performs all heavy linear-algebra operations within a semi-trusted Insider Hub, ensuring data sovereignty, low latency, and verifiable computation without revealing raw facial features. A sparse orthogonal or Toeplitz transform first obfuscates feature vectors, after which sensitive coefficients are selectively encrypted using CKKS-based polynomial encoding. Homomorphic hashing and optional zero-knowledge proofs guarantee the integrity and auditability of outsourced results. Experiments on the ORL and LFW datasets demonstrate over 94 % Rank-1 accuracy, while reducing client computation by 92 %, uplink bandwidth by 80 %, and energy usage by 55 %, with authentication latency below 120 ms on a Raspberry Pi 4-class edge device. The framework provides formal protection against IND-CPA, EUF-CMA, and IND-CCA adversaries and maintains compliance with GDPR/HIPAA requirements. Hybrid-HE LLE thus offers a scalable, secure, and real-time solution for privacy-preserving biometric access in modern IoT communication systems.