Minghui Zheng, Shicheng Huang, Deju Kong, Xing Fu · 6 authors
Linkable ring signatures are a type of ring signature scheme that can protect the anonymity of signers while allowing the public to verify whether the same signer has signed the same message multiple times. This functionality makes linkable ring signatures suitable for applications such as cryptocurrencies and anonymous voting systems, achieving the dual goals of identity privacy protection and misuse prevention. However, existing post-quantum linkable ring signature schemes often suffer from issues such as excessive linear data growth the adoption of post-quantum signature algorithms, and high circuit complexity resulting from the use of post-quantum zero-knowledge proof protocols. To address these issues, a logarithmic-size post-quantum linkable ring signature scheme based on aggregation operations is proposed. The scheme constructs a Merkle tree from ring members' public keys via a hash algorithm to achieve logarithmic-scale signing and verification operations. Moreover, it introduces, for the first time, a post-quantum aggregate signature scheme to replace post-quantum zero-knowledge proof protocols, thereby effectively avoiding the construction of complex circuits. Scheme analysis confirms that the proposed scheme meets the correctness requirements of linkable ring signatures. In terms of security, the scheme satisfies the anonymity, unforgeability, and linkability requirements of linkable ring signatures. Moreover, the aggregation process does not leak information about the signing members, ensuring strong privacy protection. Experimental results demonstrate that, when the ring size scales to 1024 members, our scheme outperforms the existing Dilithium-based logarithmic post-quantum ring signature scheme, with nearly 98.25% lower signing time, 98.90% lower verification time, and 99.81% smaller signature size.
Purpose: Automated scripts and workflows have been implemented in clinics to streamline the planning process, improving efficiency and consistency. However, standardized scripts often lack adaptability for patient-specific scenarios, requiring considerable effort to modify for non-standard cases. To address this, we present an interactive large language model (LLM)–driven approach for flexible workflow automation across radiation oncology tasks. This work presents a proof-of-concept agentic LLM integration that enables flexible, natural-language automation across a broad set of radiotherapy (RT) workflow operations. Methods: An LLM-based assistant system was integrated into the MIM software platform. It includes a recursive MIM workflow, an agentic orchestrator, and coordinated agents: an LLM Consultant for selecting relevant functions, a code generator that compiles executable Java extensions, a Quality Checker for independent verification, and a Knowledge Accumulator that captures and stores valuable insights such as coding patterns, errors, and user preferences. The system uses a prompt-based approach with continuous learning from both successful executions and error corrections to enhance accuracy and adaptability. Its generalizability was validated using 57 realistic simple queries, robustness through repeatability and failure-rate testing, and overall performance through four complex examples addressing advanced clinical tasks across various stages of the adaptive RT workflow. Results: The system effectively replicated standard clinical workflows with high adaptability and flexibility. Early queries required extensive function library accumulation, while later ones mainly reused existing functions. Its multi-agent architecture enabled robust error recovery, with automatic correction loops reducing failure rates from 1% to near zero. Average execution time per query was 13–14 s. All complex examples were successfully implemented in MIM, supporting interactive use, dynamic workflow customization, and straightforward execution. Conclusion: By integrating an interactive AI assistant, the novel LLM-powered tool provides crucial workflow flexibility alongside automation—reducing workflow rigidity, enhancing efficiency, and promising a paradigm shift toward dynamic, patient-specific treatment planning and data management.
Open access
Advanced Radiotherapy Techniques
Advances in Oncology and Radiotherapy
Artificial Intelligence in Healthcare and Education
(1) Background: The convergence of Big Data and the Internet of Things (IoT) is transforming digital accounting from retrospective documentation into real-time operational intelligence. This systematic review examines how Industry 4.0 technologies—artificial intelligence (AI), blockchain, edge computing, and digital twins—transform accounting practices through intelligent automation, continuous compliance, and predictive decision support. (2) Methods: The study synthesizes 176 peer-reviewed sources (2015–2025) selected using explicit inclusion criteria emphasizing empirical evidence. Thematic analysis across seven domains—conceptual foundations, system evolution, financial reporting, fraud detection, audit transformation, implementation challenges, and emerging technologies—employs systematic bias-reduction mechanisms to develop evidence-based theoretical propositions. (3) Results: Key findings document fraud detection accuracy improvements from 65–75% (rule-based) to 85–92% (machine learning), audit cycle reductions of 40–60% with coverage expansion from 5–10% sampling to 100% population analysis, and reconciliation effort decreases of 70–80% through triple-entry blockchain systems. Edge computing reduces processing latency by 40–75%, enabling compliance response within hours versus 24–72 h. Four propositions are established with empirical support: IoT-enabled reporting superiority (15–25% error reduction), AI-blockchain fraud detection advantage (60–70% loss reduction), edge computing compliance responsiveness (55–75% improvement), and GDPR-blockchain adoption barriers (67% of European institutions affected). Persistent challenges include cybersecurity threats (300% incident increase, $5.9 million average breach cost), workforce deficits (70–80% insufficient training), and implementation costs ($100,000–$1,000,000). (4) Conclusions: The research contributes a four-layer technology architecture and challenge-mitigation framework bridging technical capabilities with regulatory requirements. Future research must address quantum computing applications (5–10 years), decentralized finance accounting standards (2–5 years), digital twins with 30–40% forecast improvement potential (3–7 years), and ESG analytics frameworks (1–3 years). The findings demonstrate accounting’s fundamental transformation from historical record-keeping to predictive decision support.
Distributed by Grasshopper Film, 12 East 32nd St., 4th Floor, New York, NY 10016Produced by Nicholas Bruckman, Shawn Hazelett, and Rahilla ZafarDirected by Nicholas Bruckman2024, Streaming, 77 mins Minted: The Rise (And Fall?) of the NFT, directed by Nicholas Bruckman, focuses on the explosive growth of the NFT (non-fungible token) digital art market in the late 2010s and early 2020s. Bruckman introduces how NFTs are situated within broader conversations about creativity, ownership, and value in digital environments. Using interviews with artists, collectors, technologists, and cultural critics, the viewer is asked to consider how NFTs are not simply a speculative trend, but rather a disruption, a way to represent artwork as a token for transactions that bypass traditional intermediaries in the art world (like galleries and museums). The strength of the film is Bruckman’s highlighting of the experiences of artists with new opportunities for visibility and economic independence through their NFTs. Notably, the documentary amplifies the perspectives of some female and BIPOC creators, framing NFTs as a space that seems to offer alternatives to the exclusionary and gatekeeping structures of the traditional art market. While the film highlights these voices, it stops short of fully examining whether the NFT ecosystem dismantled or merely reproduced the existing inequities within the art world. Minted is a timely film for conversations about digital literacy, information ethics, and the economics of creative labor. While the documentary does not offer a comprehensive critique of blockchain systems, it is successful as a snapshot of a significant cultural moment and a useful prompt for viewers to question the intersections of art, technology, and society. Awards:SXSW, Audience Award; Next Generation Indie Film Awards, Best Documentary Feature; Cordillera International Film Festival, Grand Jury Award for Best Documentary
Kwestan Ahmed Ismael, Heshu Othman Faqe, Mohammed Hussein Abdalla, Hindreen A. Taher
In this work we use historical market data from Bitget to predict weekly open prices of Ethereum (ETH) for a 96-week period with the Prophet forecast model trained by using Particle Swarm Optimization (PSO) algorithm. Because of this, the research delves into automated hyperparameter tuning for Prophet in order to improve forecast performance on cryptocurrency markets where volatility, structural breaks and irregular trading patterns pose a significant challenge to time series prediction. The PSO algorithm is a good method to explore the high dimensional parameter space in which it can strike between the global analysis and local exploitation for detecting minimal forecast errors. Based on evaluating model performance for which we used accuracy metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) in training, test holdout & full-fit situations. PSO-optimized Prophet: The results show a great in-sample fitting and fast convergence behaviour, as the best CV RMSE is slightly higher than the lowest one should have obtained if used only 10 iterations. Although forecasts exhibit stability and track long-term trends well, the model does not predict short-term fluctuations in the holdout set with high accuracy (wider forecast uncertainty intervals). Our results shed light on the utility of PSO to improve Prophet-based price prediction in cryptocurrencies, reinforce the relevance of uncertainty quantification in asset markets and inform risk-aware decisions of financial agents dealing with unstable assets.
Shaoyu Li, Hexuan Yu, Md Mohaimin Al Barat, Yang Xiao · 6 authors
With the rise of decentralized finance, fiat-to-cryptocurrency exchange platforms have become popular entry points into the cryptocurrency ecosystem. However, these platforms frequently fail to ensure adequate privacy protection, as evidenced by real-world breaches that exposed personally identifiable information (PII) and crypto addresses. Such leaks enable adversaries to link real-world identities to cryptocurrency transactions, undermining the presumed anonymity of cryptocurrency use. We propose FC-GUARD, a privacy-preserving exchange system designed to preserve user anonymity without compromising regulatory compliance in the exchange of fiat currency for cryptocurrencies. Leveraging verifiable credentials and zero-knowledge proof techniques, FC-GUARD enables fiat-to-cryptocurrency exchanges without revealing users' PII or fiat account details. This breaks the linkage between users' real-world identities and their cryptocurrency addresses, thereby upholding anonymity, a fundamental expectation in the cryptocurrency ecosystem. In addition, FC-GUARD complies with key regulations over cryptocurrency usage, such as know-your-customer requirements and auditability for tax reporting obligations by integrating a lawful de-anonymization mechanism that allows the auditing authority to identify misbehaving users. This ensures regulatory compliance while defaulting to privacy protection. We implement our system on both desktop and mobile platforms, and our evaluation shows its feasibility for practical deployment.
The integration of IoT technology in smart grids has revolutionized the energy sector by enabling decentralized energy production, real-time monitoring, and peer-to-peer energy trading. However, these advancements introduce challenges such as ensuring security, scalability, and data privacy, which are critical for the reliable operation of IoT-enabled smart grids. Blockchain technology has emerged as a promising solution to address these challenges by providing decentralized, secure, and transparent frameworks for managing energy transactions. This study aims to explore the application of blockchain in enhancing the security and scalability of IoT-enabled smart grids while addressing challenges related to resource limitations and privacy concerns. Simulation and experimental analyses were employed to evaluate blockchain performance in a decentralized energy network. The study focused on key metrics: latency, transaction throughput, energy consumption, and data integrity. The study shows Proof of Authority (PoA) excels in IoT smart grids with < 200 ms latency, 190 Tx/s throughput, and 0.5–0.9 J/Tx energy use—outperforming PoW (450-780 ms, 5.2–10.3 J/Tx). While Proof of Stake (PoS) offers competitive 0.3–0.7 J/Tx efficiency and higher 210 Tx/s scalability, its latency (150–300 ms) remains slightly higher than PoA. These results position PoA as ideal for resource-constrained IoT nodes, while PoS better suits more extensive networks needing higher throughput. The findings highlight how consensus mechanisms can be tailored to different smart grid requirements, with PoA providing the best balance for most decentralized energy applications. Additionally, blockchain's immutable ledger ensured zero unauthorized data modifications, enhancing data security and transparency. The practical implementation of these results highlights blockchain's potential to transform IoT-enabled smart grids. By reducing security vulnerabilities and operational inefficiencies, blockchain enables secure and efficient peer-to-peer energy trading and enhances the resilience of decentralized energy systems. Future work should optimize scalability beyond 500 nodes and integrate advanced privacy-preserving mechanisms to ensure the widespread adoption of blockchain in innovative grid applications.
Student Information Management Systems (SIMS) are mission critical to higher learning institutions because they govern admissions, registration, fee status, assessment results, progression, graduation, and alumni verification. Yet conventional centralized SIMS architectures may face persistent challenges: record tampering risk, weak end‑to‑end audit trails, fragmented reconciliation across units and campuses, slow and costly credential verification, and limited interoperability with external verifiers.This study develops a conceptual and technical framework for applying Distributed Ledger Technology (DLT) to strengthen SIMS at the Tanzania Institute of Accountancy (TIA). The framework positions DLT as a trust and interoperability layer rather than a replacement for SIMS. It proposes (i) an architecture that anchors cryptographic proofs on‑chain while keeping personal data off‑chain; (ii) standards based digital credentialing using W3C Verifiable Credentials and Decentralized Identifiers; (iii) governance and compliance controls aligned to Tanzanian data protection and cybercrime regimes; and (iv) an implementation roadmap and evaluation metrics grounded in established information systems theories. To make design trade‑offs concrete, the study includes simulated calculations and figures for event volume, storage growth, verification turnaround time, and risk intensity across rollout phases. The framework provides a practical blueprint for a staged pilot at TIA starting with credential verification and assessment audit‑trail anchoring before scaling to additional workflows.
Financial Generative Pre-trained Transformers (FinGPT) with multimodal capabilities are now being increasingly adopted in various financial applications. However, due to the intellectual property of model weights and the copyright of training corpus and benchmarking questions, verifying the legitimacy of GPT's model weights and the credibility of model outputs is a pressing challenge. In this paper, we introduce a novel zkFinGPT scheme that applies zero-knowledge proofs (ZKPs) to high-value financial use cases, enabling verification while protecting data privacy. We describe how zkFinGPT will be applied to three financial use cases. Our experiments on two existing packages reveal that zkFinGPT introduces substantial computational overhead that hinders its real-world adoption. E.g., for LLama3-8B model, it generates a commitment file of $7.97$MB using $531$ seconds, and takes $620$ seconds to prove and $2.36$ seconds to verify.
The emerging occupations are organized into seven thematic domains, including artificial intelligence and data-driven work, manufacturing and robotics, blockchain and Web3 economies, green energy and sustainability, healthcare and biotechnology, space and transportation systems, and education, law, finance, and social sectors. This report is intended for researchers, policymakers, educators, and labor market analysts.
While the literature features a number of proposals to defend against transaction manipulation attacks, existing proposals are still not integrated within large blockchains, such as Bitcoin, Ethereum, and Cardano. Instead, the user community opted to rely on more practical but ad-hoc solutions (such as Mempool.space) that aim at detecting censorship and transaction displacement attacks by auditing discrepancies in the mempools of so-called observers. In this paper, we precisely analyze, for the first time, the interplay between mempool auditing and the ability to detect censorship and transaction displacement attacks by malicious miners in Bitcoin and Ethereum. Our analysis shows that mempool auditing can result in mis-accusations against miners with a probability larger than 25% in some settings. On a positive note, however, we show that mempool auditing schemes can successfully audit the execution of any two transactions (with an overwhelming probability of 99.9%) if they are consistently received by all observers and sent at least 30 seconds apart from each other. As a direct consequence, our findings show, for the first time, that batch-order fair-ordering schemes can offer only strong fairness guarantees for a limited subset of transactions in real-world deployments.
Verifiable delegation in digital identity systems remains unresolved across centralized, federated, and self-sovereign identity (SSI) environments, particularly where both human users and autonomous AI agents must exercise and transfer authority without exposing primary credentials or private keys. We introduce a unified framework that enables bounded, auditable, and least-privilege delegation across heterogeneous identity ecosystems. The framework includes four key elements: Delegation Grants (DGs), first-class authorization artefacts that encode revocable transfers of authority with enforced scope reduction; a Canonical Verification Context (CVC) that normalizes verification requests into a single structured representation independent of protocols or credential formats; a layered reference architecture that separates trust anchoring, credential and proof validation, policy evaluation, and protocol mediation via a Trust Gateway; and an explicit treatment of blockchain anchoring as an optional integrity layer rather than a structural dependency. Together, these elements advance interoperable delegation and auditability and provide a foundation for future standardization, implementation, and integration of autonomous agents into trusted digital identity infrastructures.
Deepesh Khatak, Karan Rathode, Assistant professor Ms. Maanvika
Blockchain technology has emerged as a transformative paradigm for secure, decentralized, and transparent data management across various domains, including finance, supply chain, healthcare, and governance. At its core, blockchain operates as a distributed ledger that ensures data integrity through cryptographic techniques, consensus mechanisms, and decentralized network architecture. This paper presents a comprehensive overview of blockchain architecture and its fundamental security foundations. It explains the structural components of a blockchain system, such as blocks, transactions, hash functions, Merkle trees, peer-to-peer networks, and consensus protocols, highlighting their roles in maintaining trust without reliance on centralized authorities. The study further examines key security principles that underpin blockchain systems, including immutability, transparency, fault tolerance, and resistance to tampering. Common security threats and attack vectors—such as double-spending attacks, 51% attacks, Sybil attacks, and smart contract vulnerabilities—are discussed to provide insight into potential risks faced by blockchain networks. In addition, the paper explores cryptographic mechanisms such as public-key encryption, digital signatures, and hashing algorithms that contribute to secure transaction validation and identity management. By integrating architectural analysis with security considerations, this work aims to build a strong foundational understanding of how blockchain systems achieve trust, resilience, and data integrity in decentralized environments. The paper serves as a valuable reference for students, researchers, and practitioners seeking to understand the core architectural design and security challenges of blockchain technology, as well as its potential for secure and scalable real-world applications.
Arus Reka Prasetia, Primanola Perdananti, Ikaputera Waspada, Maya Macia Sari
Agency conflicts remain a persistent challenge in corporate governance because information asymmetry and misaligned incentives can weaken monitoring and accountability. This systematic literature review synthesizes international empirical evidence on how blockchain and smart contracts relate to agency conflict mitigation and governance outcomes, and it clarifies boundary conditions and implications for Agency Theory. We followed PRISMA reporting guidance and searched Scopus for English journal articles published between 2018 and 2025. After title, abstract, and full-text screening, 13 empirical studies were included for quality appraisal and thematic narrative synthesis. Across contexts, blockchain adoption or innovation intensity is most consistently associated with improved information environments, including higher transparency and reporting quality and lower opportunism related proxies, and it is also associated with improved investment efficiency and selected compliance and risk outcomes. Evidence on smart contracts is substantially thinner. Smart contracts are explicitly analysed in one case study and they are discussed secondarily in one additional study, while none of the large sample quantitative studies operationalises smart contract use as a distinct construct. The synthesis indicates that governance benefits depend on data integrity supported by internal controls, external monitoring and assurance capacity, and regulatory and legal alignment that enables auditability and enforceability. Overall, blockchain-enabled corporate governance is best interpreted as governance by system design that complements conventional mechanisms and motivates future research on measurable smart contract use cases and stronger causal identification.
Bintang Sahala Marpaung, Annaria Magdalena Marpaung, Petrosina Chece
Accurate stock price forecasting is vital for investors in formulating rational investment decisions within capital markets. This study analyzes the impact of Bitcoin, interest rates, and exchange rates on the stock prices of firms in the oil and gas mining sub-sector listed on the Indonesia Stock Exchange over the period 2018–2023. Employing a quantitative research design, the study utilizes secondary data and applies panel data regression analysis using EViews 9. The sample consists of eight firms selected from a population of eighteen companies through purposive sampling. The empirical results reveal that Bitcoin exerts a statistically significant partial effect on stock prices, whereas interest rates and exchange rates do not demonstrate a significant individual impact. Furthermore, the joint analysis indicates that Bitcoin, interest rates, and exchange rates collectively have no significant influence on stock prices. These findings suggest that investors should carefully assess stock price movements and broader market dynamics when making investment decisions, while firms are encouraged to enhance their financial performance to improve investment attractiveness.
High-frequency crypto forecasting requires systems that are accurate, explainable, and designed for human decision-making. Bitcoin presents a unique challenge for Human-Centred AI (HCAI) due to its volatility and sensitivity to heterogeneous technical, fundamental, and sentiment signals. This paper presents an explainable multimodal framework for Bitcoin forecasting at 15-minute resolution. We align five modalities—market data, on-chain metrics, the Fear & Greed Index (FGI), news, and Reddit—onto a unified, leakage-safe 15-minute grid. We evaluate tree-based, sequential, and Multimodal Fusion Block (MFB) models for next-interval log-return prediction using chronological splits. Results show that while short-horizon prediction remains challenging, multimodal features consistently improve over structured baselines, particularly during event-driven periods. To ensure transparency, the framework integrates a dual-layer explanation system: SHapley Additive exPlanations (SHAP) attributions combined with large language model (LLM) narratives, ensuring outputs are both technically faithful and human-accessible. This work unlocks the “black box” of complex predictive architectures, transforming opaque multimodal signals into transparent, actionable decision support for high-frequency trading.
Confronted with the challenges of rapid urbanization and environmental pressures, this thesis addresses the critical limitations of current digital-twin platforms in integrating heterogeneous data streams—from air/water quality sensors to healthcare and infrastructure—into a unified, actionable model for smart cities. We propose and validate a trustworthy digital twin monitoring system, built upon a foundational framework of semantic data models and ontologies that enable data fusion, provenance tracking, and ML-driven decision support. This framework is extended by two core mechanisms: (i) .soda, a self-sovereign data attestation protocol using zero-knowledge proofs to ensure verifiable authenticity, and (ii) G-TOK, a privacy-preserving proof framework for sensor verification and multi-agent trust consensus. Together, they form a Mechanism and Verification Layer guaranteeing data integrity and fault-tolerant control. The architecture integrates adaptive time-series ML pipelines for predictive analytics, a semantic data model based on knowledge graphs, and a game-theoretic fusion algorithm with Byzantine fault tolerance.Evaluated through smart-city pilot studies in river-water monitoring, the system demonstrates enhanced fault tolerance, adaptive data compensation, and resilient consensus under the Trustworthy Game-Theoretic Framework (TGTF). This end-to-end methodology spans data aggregation/fusion, semantic data supply chains, AI agents, and the TGTF tailored for low-cost networks. The TGTF implements a closed-loop process of anomaly detection, error identification, and data compensation, boosting reliability while cutting costs. Furthermore, we introduce a trustworthy data model that aggregates analytical signals to assess environmental impact and integrates generative AI into cognitive digital twins to autonomously generate data supply chains, pioneering continuous parallel intelligence. This interdisciplinary work establishes the methodological and engineering foundations for Trustworthy Digital Twins (TDTs), offering a scalable, secure, and predictive solution for environmental and public-health monitoring. It paves the way for reproducible, scalable, and actionable urban intelligence, with future work aimed at coordinating multiple AI agents to strengthen urban resilience.
The Ambazonian conflict in Cameroon’s Anglophone regions has unfolded within an era defined by mobile connectivity, social media, and digitally mediated political contention. This article examines the role of mobile technology in shaping the conflict’s trajectory from early mobilization to prolonged armed stalemate. It argues that mobile technology functioned as both an enabling and destabilizing force: facilitating mass mobilization, diaspora coordination, documentation of abuses, and digital finance, while simultaneously accelerating fragmentation, disinformation, cybersecurity exposure, and state surveillance. Drawing on comparative cases including the Arab Spring, ISIS, Ukraine, Ethiopia, and Myanmar, the article situates Ambazonia within broader patterns of digital contention and digital authoritarian response. The analysis further demonstrates how ungoverned digital visibility and decentralized online fundraising undermined strategic coherence and legitimacy. The article concludes that while mobile technology can amplify resistance, it cannot substitute for political legitimacy, institutional coherence, or negotiated settlement. Durable peace requires a transition from networked resistance to normatively grounded frameworks such as the Alliance for Peace and Justice (APJ) Peace Plan.
This paper introduces a cybersecurity framework that combines a deception-based ransomware detection system, called the Intrusion and Ransomware Detection System for Cloud (IRDS4C), with a blockchain-enabled Cyber Threat Intelligence platform (CTIB). The framework aims to improve the detection, reporting, and sharing of ransomware threats in cloud environments. IRDS4C uses deception techniques such as honeypots, honeytokens, pretender network paths, and decoy applications to identify ransomware behavior within cloud systems. Tests on 53 Windows-based ransomware samples from seven families showed an ordinary detection time of about 12 s, often quicker than tralatitious methods like file hashing or entropy analysis. These detection results are currently limited to Windows-based ransomware environments, and do not yet cover Linux, containerized, or hypervisor-level ransomware. Detected threats are formatted using STIX/TAXII standards and firmly shared through CTIB. CTIB applies a hybrid blockchain consensus of Proof of Stake (PoS) and Proof of Work (PoW) to ensure data integrity and protection from tampering. Security analysis shows that an attacker would need to control over 71% of the network to compromise the system. CTIB also improves trust, accuracy, and participation in intelligence sharing, while smart contracts control access to erogenous data. In a local prototype deployment (Hardhat devnet + FastAPI/Uvicorn), CTIB achieved 74.93–125.92 CTI submissions/min, The number of attempts or requests in each test was 100 with median end-to-end latency 455.55–724.99 ms (p95: 577.68–1364.17 ms) across PoW difficulty profiles (difficulty_bits = 8–16).
본 논문은 장애, 공격, 검열, 네트워크 분할과 같은 "중단 사건" 이후 분산 원장 및 합의 시스템이 어떻게 재수렴하는지 분석하기 위한 개념적 프레임워크로 흐름 복원력(Flow-Resilience )을 제안합니다 . "칼로 물을 베어도 물은 계속 흐른다"는 고전 속담에 기반하여, 본 논문은 속담과 프로토콜 간의 매핑 템플릿과 흐름→축소→분할→재흐름 단계 모델을 제시하고, 나카모토식 합의, 팍소스 계열 합의, BFT 복제 방식 간의 재수렴 경로를 비교합니다. 단, 구현상의 민감한 세부 정보는 공개하지 않습니다. License and Usage Notice Proprietary — All Rights Reserved This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). To view a copy of this license, visit: http://creativecommons.org/licenses/by-nc-nd/4.0/ Key Constraints: Attribution: You must give appropriate credit to the author (Jeongchoon Lee). Non-Commercial: You may not use this material for commercial purposes (including corporate-funded research or patent filings by third parties). NoDerivatives (No-Derivs): If you remix, transform, or build upon the material, you may not distribute the modified material. The production of derivative works—including academic papers that hybridize this theory with other frameworks—is strictly prohibited without the express written consent of the author.
Zhichao Wang, Xudong Zhu, Xinxuan Zhang, Yi Deng · 5 authors
Abstract Time-release cryptography is a flourishing research area with a long history and has been extensively studied. In this work, we enrich it by introducing a novel concept: a time-release zero-knowledge proof (TRZKP). A TRZKP is a non-interactive zero-knowledge proof that allows one to publish a proof for a given relation $$R_\mathcal {L}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>R</mml:mi> <mml:mi>L</mml:mi> </mml:msub> </mml:math> , such that anyone can only finish the verification after time $$\textbf{T}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>T</mml:mi> </mml:math> by performing a sequential computation. This work formalizes the concept of TRZKP and presents light constructions for the time-release version of any NIZK obtained from a public-coin protocol via Fiat-Shamir transformation. TRZKPs can be applied to provide time-release authentication, for example, they can be employed to construct verifiable timed signatures (VTS), introduced by Thyagarajan et al. (CCS’20). Through both theoretical and practical analysis, our construction has advantages over existing VTS for Fiat-Shamir signatures. Specifically, when instantiated with Shnorr signature, our VTS signing time remains basically unchanged as the delay time grows, and is preferable for longer delay times; our VTS verification time is significantly small (on the level of milliseconds, while existing works on the level of seconds), and our VTS size is 67 times smaller than the state-of-the-art. It also has the time-verifiability property, which ensures the signature is recoverable after the specified time.