Effective supply chain management under high-variance demand requires models that jointly address demand uncertainty and digital contracting adoption. Existing research often simplifies demand variability or treats adoption as an exogenous decision, limiting relevance in e-commerce and humanitarian logistics. This study develops an optimization framework combining dynamic Negative Binomial (NB) demand modeling with endogenous smart contract adoption. The NB process incorporates autoregressive dynamics in success probability to capture overdispersion and temporal correlation. Simulation experiments using four real-world datasets, including Delhivery Logistics and the SCMS Global Health Delivery system, apply maximum likelihood estimation and grid search to optimize adoption intensity and order quantity. Across all datasets, the NB specification outperforms Poisson and Gaussian benchmarks, with overdispersion indices exceeding 1.5. Forecasting comparisons show that while ARIMA and Exponential Smoothing achieve similar point accuracy, the NB model provides superior stability under high variance. Scenario analysis reveals that when dispersion exceeds a critical threshold (r > 6), increasing smart contract adoption above 70% significantly enhances profitability and service levels. This framework offers actionable guidance for balancing inventory costs, service levels, and implementation expenses, highlighting the importance of aligning digital adoption strategies with empirically observed demand volatility.
Organisation scholars frequently refer to Franz Kafka to shed light on various dark sides of organisations, in particular the dysfunctional aspects of bureaucratic organisations commonly associated with the word “Kafkaesque.” In this essay, we take the hundredth anniversary of Franz Kafka’s death as an occasion to revisit his work and life as a source of imagination for organisation scholars. We start by introducing Kafka’s “office writings,” produced during his daytime job as an accident insurance lawyer for industrial workers and, so far, largely overlooked by organisation scholars, as well as facets of his biography. We then propose that a more comprehensive analysis of Kafka’s oeuvre offers organisation scholarship a unique perspective on two pressing, contemporary challenges of organising. First, Kafka’s work and life illuminate the inherent contingency and futility of organising in the face of uncertainty, while also highlighting its necessity. Second, his writing provides a nuanced understanding of enigmatic, inescapable organisations that resonate with today’s digital and algorithmic forms of organising. Through his work, we find examples of individual and organisational acts of resilience and resistance, including a leveraging of bureaucratic institutions to fight inequality and injustice. These themes directly speak to current debates on the role of organisation in times of crisis and disruption, marked by the erosion of democratic institutions, the rise of digital and algorithmic organizing, and ecological collapse.
Decentralized Autonomous Organizations (DAOs) aim to enable participatory governance, but in practice face challenges of voter apathy, concentration of voting power, and misaligned delegation. Existing delegation mechanisms often reinforce visibility biases, where a small set of highly ranked delegates accumulate disproportionate influence regardless of their alignment with the broader community. In this paper, we conduct an empirical study of delegation in DAO governance off-chain discussions from 14 DAO forums. We develop a methodology to link forum participants to on-chain addresses, extract governance interests using large language models, and compare these interests against delegates' historical behavior. Our analysis reveals that delegations are frequently misaligned with token holders' expressed priorities and that current ranking-based interfaces exacerbate power concentration. We argue that incorporating interest alignment into delegation processes could mitigate these imbalances and improve the representativeness of DAO decision-making. To support future research, we will release our dataset and code in a public repository.
This article analyzes the implementation of participatory budgeting as a democratic governance mechanism in the Decentralized Autonomous Municipal Government of Santa Ana de Cotacachi, Ecuador, during 2015-2020. Through a mixed methodological approach combining systematic documentary analysis of 147 official documents, 42 semi-structured interviews, participant observation in 12 deliberative sessions, and statistical analysis of management indicators, the institutionalization of the Cantonal Participation System and its multidimensional impact on local public management is examined. Results show that normative formalization through the 2016 Ordinance consolidated a two-decade participatory process, achieving mobilization of 8,347 unique citizens (21.3% of adult population) and execution of 312 priority projects with investment exceeding USD 8.4 million. Econometric analysis reveals significant correlation (r=0.76, p<0.001) between national transfers and participatory resources, evidencing structural fiscal vulnerability. Three critical success factors are identified: multilevel institutional articulation, accumulated social capital (186 active organizations), and contextualized methodological adaptation. However, structural challenges persist related to fiscal dependency (68% of revenue from transfers), power asymmetries in participation (73% male overrepresentation in leadership), and tensions between economic development and environmental sustainability. Comparative analysis with 15 Latin American experiences positions Cotacachi as a paradigmatic case of institutional innovation, albeit with scalability limitations. The study concludes that the model represents a significant contribution to democratization of subnational public management, requiring structural reforms in fiscal architecture and inclusion mechanisms to ensure sustainability.
Decentralized applications (dApps) in Decentralized Finance (DeFi) face a fundamental tension between regulatory compliance requirements like Know Your Customer (KYC) and maintaining decentralization and privacy. Existing permissioned DeFi solutions often fail to adequately protect private attributes of dApp users and introduce implicit trust assumptions, undermining the blockchain's decentralization. Addressing these limitations, this paper presents a novel synthesis of Self-Sovereign Identity (SSI), Zero-Knowledge Proofs (ZKPs), and Attribute-Based Access Control to enable privacy-preserving on-chain permissioning based on decentralized policy decisions. We provide a comprehensive framework for permissioned dApps that aligns decentralized trust, privacy, and transparency, harmonizing blockchain principles with regulatory compliance. Our framework supports multiple proof types (equality, range, membership, and time-dependent) with efficient proof generation through a commit-and-prove scheme that moves credential authenticity verification outside the ZKP circuit. Experimental evaluation of our KYC-compliant DeFi implementation shows considerable performance improvement for different proof types compared to baseline approaches. We advance the state-of-the-art through a holistic approach, flexible proof mechanisms addressing diverse real-world requirements, and optimized proof generation enabling practical deployment.
Xingxing Chen, Xiaohong Zhang, Shaojiang Zhong, Shuling Liu
Vehicular Ad Hoc Networks (VANETs) are now a pivotal component of Intelligent Transportation Systems. However, ensuring secure vehicle identity authentication and protecting user privacy remain two challenging issues in VANETs. Addressing these challenges, this paper seamlessly integrates blockchain technology with the InterPlanetary File System to realize a fully decentralized storage solution for identity verification information. Simultaneously, it employs zk-SNARK and elliptic curve cryptography to allow vehicle users to anonymously complete identity verification. Additionally, the lightweight identity authentication proof obtained after successful verification maintains credibility while reducing the computational and communication costs for both roadside units and vehicles. The security and performance analysis of the system show that the proposed scheme has significant advantages in both communication and computation compared with similar research, while also offering superior security and a broader range of functional attributes compared to existing competitive approaches.
Zero-Knowledge Ethereum Virtual Machines (zkEVMs) must reconcile an inherent tension. The Ethereum Virtual Machine (EVM) was designed for transparent step-by-step execution with dynamic control flow. Proving such execution in zero-knowledge, however, requires transforming it into algebraic circuit representations that encode computation as mathematical constraints. Existing surveys address zkEVMs at the level of implementations, cryptographic primitives, or Layer 2 deployment, leaving the constraint-system design that governs their cost largely unexamined. This survey provides the first constraint-level analysis of how five production zkEVM systems and three universal Zero-Knowledge Virtual Machines (zkVMs) resolve this tension through constraint engineering. We show that the degree of EVM compatibility, captured by the Type 1-4 spectrum, is the defining architectural decision that shapes all subsequent technical choices. We classify the design space along four architectural dimensions, namely arithmetization frameworks, dispatch strategies, semantic rewrites, and recursion approaches. Examining the mechanisms within each dimension, we identify the technical factors and trade-offs that drive each choice. The analysis reveals that all five surveyed production zkEVMs adopt PLONKish arithmetization. The zkVMs instead rely on the Algebraic Intermediate Representation (AIR), which suits uniform state machines. A single trade-off between EVM compatibility and constraint cost underlies these choices. The most Ethereum-equivalent systems accept higher constraint counts to preserve full bytecode fidelity, while systems that relax that fidelity attain substantially lower constraint counts. We close with the critical open problems and future research directions that this constraint-level view brings into focus.
Recent advancements in large language models (LLMs) and agentic systems have shown exceptional decision-making capabilities, revealing significant potential for autonomic finance. Current financial trading agents predominantly simulate anthropomorphic roles that inadvertently introduce emotional biases and rely on peripheral information, while being constrained by the necessity for continuous inference during deployment. In this paper, we pioneer the harmonization of strategic depth in agents with the mechanical rationality essential for quantitative trading. Consequently, we present TiMi (Trade in Minutes), a rationality-driven multi-agent system that architecturally decouples strategy development from minute-level deployment. TiMi leverages specialized LLM capabilities of semantic analysis, code programming, and mathematical reasoning within a comprehensive policy-optimization-deployment chain. Specifically, we propose a two-tier analytical paradigm from macro patterns to micro customization, layered programming design for trading bot implementation, and closed-loop optimization driven by mathematical reflection. Extensive evaluations across 200+ trading pairs in stock and cryptocurrency markets empirically validate the efficacy of TiMi in stable profitability, action efficiency, and risk control under volatile market dynamics.
Web 3.0 represents the next generation of the Internet, which is widely recognized as a decentralized ecosystem that focuses on value expression and data ownership. By leveraging blockchain and artificial intelligence technologies, Web 3.0 offers unprecedented opportunities for users to create, own, and monetize their content, thereby enabling User-Generated Content (UGC) to an entirely new level. However, some self-interested users may exploit the limitations of content curation mechanisms and generate low-quality content with less effort, obtaining platform rewards under information asymmetry. Such behavior can undermine Web 3.0 performance. To this end, we propose \textit{LMM-Incentive}, a novel Large Multimodal Model (LMM)-based incentive mechanism for UGC in Web 3.0. Specifically, we propose an LMM-based contract-theoretic model to motivate users to generate high-quality UGC, thereby mitigating the adverse selection problem from information asymmetry. To alleviate potential moral hazards after contract selection, we leverage LMM agents to evaluate UGC quality, which is the primary component of the contract, utilizing prompt engineering techniques to improve the evaluation performance of LMM agents. Recognizing that traditional contract design methods cannot effectively adapt to the dynamic environment of Web 3.0, we develop an improved Mixture of Experts (MoE)-based Proximal Policy Optimization (PPO) algorithm for optimal contract design. Simulation results demonstrate the superiority of the proposed MoE-based PPO algorithm over representative benchmarks in the context of contract design. Finally, we deploy the designed contract within an Ethereum smart contract framework, further validating the effectiveness of the proposed scheme.
Lau Wen Xuan, Shamsul Kamal Ahmad Khalid, Lokman Mohd Fadzil
Existing donation platforms suffer from limited transparency, centralized governance, and minimal donor participation in fund allocation decisions. Donors often lack visibility into how their contributions are used and have no role in prioritizing charitable projects. These shortcomings undermine trust and reduce engagement. In this paper, a DAObased blockchain donation system is proposed to address these challenges by enabling decentralized, transparent, and automated governance. Built on the Ethereum blockchain using Ganache, MetaMask, and Solidity smart contracts, the system allows verified donors to vote on project prioritization. Smart contracts autonomously execute fund distribution based on vote proportions, removing administrative discretion. The platform consists of eight functional modules, developed using Agile methodology, and implements key DAO features such as token-based eligibility, one-person-one-vote enforcement, and trustless execution. Functional testing validated 24 key system features, while security tests confirmed strong password policies, MetaMask-based identity, and reCAPTCHA protection. User acceptance testing with total 20 donors and 15 administrators resulted in satisfaction scores of 4–5 on a 5-point Likert scale. This research demonstrates how DAO principles can be applied to real-world philanthropic ecosystems, transforming donation systems from passive funding channels into participatory, community-governed platforms.
P Kalaiarasi, C. Vignesh, Gajarla Harshitha, K. Abhiram · 5 authors
Secure, transparent, and tamper-proof voting systems are essential to maintain democratic values and public confidence. Conventional paper ballot and electronic voting systems are prone to problems like fraud, manipulation, double voting, and transparency issues. This study envisions a blockchain-based voting system based on the Ethereum platform and smart contracts to guarantee decentralization, immutability, and verifiability. The system implements multi-layered authentication using email-based OTP verification, cryptographic hashing of the vote, blockchain-like ledger entries, and a distinct voting receipt token to preserve privacy and accountability. Real-time visualization of results using Chart.js and an interactive front-end with Tailwind CSS improves usability. In contrast to existing literature, our system incorporates secure receipt verification and live dashboards, decreasing dependency on intermediaries while guaranteeing trust. Experimental testing reveals that the system enhances election security through blocking double votes, facilitating real-time auditing, and providing tamper-proof evidence. Its future extensions involve biometric verification, mobile and remote voting, and integration into national ID systems for large-scale adoption. This paper illustrates the potential of blockchain in transforming electoral procedures and overcoming transparency, accessibility, and auditability issues.
Blockchain technology has revolutionized decentralized systems, with applications that span finance, healthcare, and supply chain management. However, scalability challenges, particularly limited transaction throughput and high computational overhead, hinder its broader adoption. This work presents and validates a machine learning-driven consensus mechanism to enhance scalability while preserving security and decentralization. The proposed approach dynamically adjusts the mining difficulty and resource allocation in real time by employing Bayesian-optimized ensemble models (XGBoost and Random Forest) to predict the conditions of the blockchain network. Experimental evaluations show improved throughput, lower latency, and more equitable miner participation compared to traditional Proof of Work (PoW). The findings suggest that data-driven consensus can mitigate long-standing performance bottlenecks, enabling next-generation decentralized systems for industrial-scale deployment.
Centralized exchanges (CEXs) currently dominate the cryptocurrency trading landscape due to their speed, liquidity, and ease of use. However, they also introduce several critical risks, including custodianship of user assets, vulnerability to censorship, and reliance on centralized infrastructure that represents a single point of failure. In contrast, the advent of Automated Market Makers (AMMs), such as Uniswap, brought a paradigm shift in decentralized finance (DeFi) by enabling peer-to-peer trading through liquidity pools without intermediaries. While revolutionary, AMMs face inherent limitations such as slippage, impermanent loss for liquidity providers, and suboptimal price discovery compared to traditional orderbook systems. This research proposes a decentralized on-chain orderbook model designed to bridge the gap between centralized exchanges and AMM-based decentralized exchanges. The system replicates the precision, transparency, and efficiency of traditional orderbookdriven markets while adhering to DeFi principles of trustlessness and non-custodial asset management. Developed using Solidity smart contracts and deployed on Ethereum-compatible test networks such as Monad the platform enables users to place, cancel, and execute both limit and market orders directly on-chain. To address blockchain performance bottlenecks, the architecture incorporates an off-chain order matcher that listens to smart contract events, identifies compatible buy and sell orders, and batches potential matches for improved gas efficiency. Importantly, final trade execution and settlement remain fully decentralized, being handled exclusively by smart contracts. This hybrid design achieves low-latency order matching without compromising decentralization or asset security
Computer-aided cryptography, with particular emphasis on formal verification, promises an interesting avenue to establish strong guarantees about cryptographic primitives. The appeal of formal verification is to replace the error-prone pen-and-paper proofs with a proof that was checked by a computer and, therefore, does not need to be checked by a human. In this paper, we ask the question of how reliable are these machine-checked proofs by analyzing a formally verified implementation of the Line-Point Zero-Knowledge (LPZK) protocol (Dittmer, Eldefrawy, Graham-Lengrand, Lu, Ostrovsky and Pereira, CCS 2023). The implementation was developed in EasyCrypt and compiled into OCaml code that was claimed to be high-assurance, i.e., that offers the formal guarantees of guarantees of completeness, soundness, and zero knowledge. We show that despite these formal claims, the EasyCrypt model was flawed, and the implementation (supposed to be high-assurance) had critical security vulnerabilities. Concretely, we demonstrate that: 1) the EasyCrypt soundness proof was incorrectly done, allowing an attack on the scheme that leads honest verifiers into accepting false statements; and 2) the EasyCrypt formalization inherited a deficient model of zero knowledge for a class of non-interactive zero knowledge protocols that also allows the verifier to recover the witness. In addition, we demonstrate 3) a gap in the proof of the perfect zero knowledge property of the LPZK variant of Dittmer, Ishai, Lu and Ostrovsky (CCS 2022) that the EasyCrypt proof is based, which, depending on the interpretation of the protocol and security claim, could allow a malicious verifier to learn the witness. Our findings highlight the importance of scrutinizing machine-checked proofs, including their models and assumptions. We offer lessons learned for both users and reviewers of tools like EasyCrypt, aimed at improving the transparency, rigor, and accessibility of machine-checked proofs. By sharing our methodology and challenges, we hope to foster a culture of deeper engagement with formal verification in the cryptographic community.
Traceable Ring Signatures (TRS) were introduced by Fujisaki and Suzuki~[PKC'07], where a trace algorithm can publicly check if two signatures with the same event label were generated by the same signer (linkability). In addition, if the two signatures correspond to different messages, then the signer's identity is revealed (traceability). Following [PKC'07], most subsequent works adopt the same definitions and consider three security properties, anonymity, linkability, and exculpability. [PKC'07] proved that the latter two properties together imply unforgeability, a fundamental requirement for all signature-like primitives. ~~~~In this work, we identify a gap in the aforementioned proof, which arises from the insufficient consideration of linkability and exculpability in [PKC'07]. To address this, we revisit the syntax and security notions of TRS, and close this gap by defining extended linkability and extended exculpability. Building on these, we design a new framework of TRS from PseudoRandom Functions (PRF) and Zero-Knowledge Proofs of Knowledge (ZKPoK) that supports tracing, provided that both two signatures are valid. This constitutes a substantial improvement over existing approaches---all of which require tracing with the size of the ring---and elevates TRS to a level of practicality and efficiency comparable to Linkable Ring Signatures (LRS), which have already achieved widespread deployment in practice. Finally, we instantiate our generic framework from the DDH assumption and leverage the Bulletproofs [S\&P'18] to construct a TRS scheme with log-size signatures. The proposed scheme achieves highly optimized signature sizes in practice and remains compatible with most existing DLog-based systems. On Curve25519, the signature size is bytes, which to our best knowledge is the shortest LRS scheme for a ring .
This paper focuses on the research of auditing data privacy protection mechanism under blockchain technology and constructs an efficient computational model. The model is based on the distributed ledger characteristic of blockchain, and ensures the data tampering and traceability by optimizing the consensus mechanism. In the proposed model, the consensus mechanism is optimized by utilizing the tamper proof properties of blockchain. By building a multi node collaborative framework that supports batch auditing, this model improves data synchronization efficiency. The focus of optimization is to reduce the consensus reaching time and thus improve the overall performance and scalability of the blockchain network. At the same time, smart contracts are utilized to realize automated data sharing and auditing processes to improve auditing efficiency. In terms of data encryption algorithm design, a chaotic system based on RSA algorithm encryption is designed by combining the randomness and complexity of chaos theory to further strengthen the security of data. The results show that the model in this paper can make the ciphertext image uncorrelated in all directions and improve the encryption strength of the image. The method of this paper can make the audit data information on the degree of privacy protection and the complexity of the ciphertext image increase up to about 50% and 45% than the comparison method; the encryption and decryption time of the data is reduced by about 20 s. In addition, the running time of the stages of this paper’s algorithm increases with the increase in the number of concurrent requests and this paper’s system can support concurrency of 500 users at the same time and make its throughput up to 583.49/s.
Blind signatures have become a cornerstone for privacy-sensitive applications such as digital cash, anonymous credentials, and electronic voting. The elliptic curve variant of the Digital Signature Algorithm (ECDSA) is widely adopted due to its efficiency in resource-constrained environments, such as mobile devices and blockchain systems. Building blind ECDSA is hence a natural goal. One presents the first such construction relying solely on the ECDSA assumption. Despite the inherent complexities in integrating blindness with ECDSA, we design a protocol that ensures both unforgeability and blindness without introducing new computational assumptions and ensuring concurrent security. It involves zero-knowledge proofs based on the MPC-in-the-head paradigm for complex statements combining relations on encrypted elliptic curve points, their coordinates, and discrete logarithms.
Xiaoxue Zhang, Sammy Tesfai, Minmei Wang, Haofan Cai
Multi-enterprise applications in fields like supply chain management, finance, and healthcare require complex collaboration and data exchange among organizations to ensure operational efficiency and build trust. Permissioned blockchains emerged as a promising solution, providing shared, immutable ledgers that enhance transparency, traceability, and trust among authorized parties. However, during asset trading between organizations, they must verify the legitimacy of asset transfers, including asset ownership and quantity, while protecting sensitive asset owner information. To achieve both verifiability and privacy, this paper introduces PAVE, Privacy-preserving Aggregated Verification system for Multi-Enterprises Blockchain, a framework that integrates zero-knowledge proofs to enable secure asset verification without breaking user privacy. To achieve proof efficiency, PAVE introduces a proof aggregation mechanism that consolidates multiple transaction verifications into a single proof, significantly reducing computational overhead for large-scale scenarios. Evaluation results show that, with the proof aggregation mechanism, PAVE achieves low verification latency and resource utilization, making it a scalable solution for privacy-preserving asset verification across multiple enterprises.
James Bartusek, Ruta Jawale, Justin Raizes, Kabir Tomer
We construct a publicly-verifiable non-interactive zero-knowledge argument system for QMA with the following properties. 1. Transparent setup. Our protocol only requires a uniformly random string (URS) setup. The only prior publicly-verifiable NIZK for QMA (Bartusek and Malavolta, ITCS 2022) requires an entire obfuscated program as the common reference string. 2. Extractability. Valid QMA witnesses can be extracted directly from our accepting proofs. That is, we obtain a publicly-verifiable non-interactive argument of quantum knowledge, previously only known in a privately-verifiable setting (Coladangelo, Vidick, and Zhang, CRYPTO 2020). Our construction introduces a novel ZX QMA verifier with "strong completeness" and builds upon the coset state authentication scheme from (Bartusek, Brakerski, and Vaikuntanathan, STOC 2024) within the context of QMA verification. Along the way, we establish new properties of the authentication scheme. The security of our construction rests on the heuristic use of a post-quantum indistinguishability obfuscator. Rather than rely on the full-fledged classical oracle model (i.e. ideal obfuscation), we isolate a particular game-based property of the obfuscator that suffices for our proof, which we dub the evasive composability heuristic. As an additional contribution, we study a general method for replacing heuristic use of obfuscation with heuristic use of hash functions in the post-quantum setting. In particular, we establish security of the ideal obfuscation scheme of Jain, Lin, Luo, and Wichs (CRYPTO 2023) in the quantum pseudorandom oracle model (QPrO), which can be heuristically instantiated with a hash function. This gives us NIZK arguments of quantum knowledge for QMA in the QPrO, and additionally allows us to translate several quantum-cryptographic results that were only known in the classical oracle model to results in the QPrO.
We present a cross-market algorithmic trading system that balances execution quality with rigorous compliance enforcement. The architecture comprises a high-level planner, a reinforcement learning execution agent, and an independent compliance agent. We formulate trade execution as a constrained Markov decision process with hard constraints on participation limits, price bands, and self-trading avoidance. The execution agent is trained with proximal policy optimization, while a runtime action-shield projects any unsafe action into a feasible set. To support auditability without exposing proprietary signals, we add a zero-knowledge compliance audit layer that produces cryptographic proofs that all actions satisfied the constraints. We evaluate in a multi-venue, ABIDES-based simulator and compare against standard baselines (e.g., TWAP, VWAP). The learned policy reduces implementation shortfall and variance while exhibiting no observed constraint violations across stress scenarios including elevated latency, partial fills, compliance module toggling, and varying constraint limits. We report effects at the 95% confidence level using paired t-tests and examine tail risk via CVaR. We situate the work at the intersection of optimal execution, safe reinforcement learning, regulatory technology, and verifiable AI, and discuss ethical considerations, limitations (e.g., modeling assumptions and computational overhead), and paths to real-world deployment.
This study examines values and adoption conditions of Blockchain Technology (BCT) in horizontal demand forecast sharing among retailer, focusing on the influence mechanism of transparency-restriction approaches and BCT's endogenous effects on firms' sharing incentives. We model a supply chain with one manufacturer and multiple retailers, comparing four BCT-enabled data-sharing regimes: open access (permissionless) versus no-open access (permissioned), with or without encryption. Results show that restricted transparency, combined with selective accessibility, aligns individual and collective incentives by curbing wholesale price inflation and improving forecast accuracy. Contrary to intuition, higher transparency does not universally benefit retailers; supplementary encryption can balance data utility and privacy, enabling Pareto-superior outcomes. We further demonstrate BCT can reduces moral hazards in horizontal sharing (e.g. sharing biased forecast), allowing retailers to leverage aggregated demand signals without inefficiently verification. However, excessive transparency in BCT can accelerates retailers' profit erosion, akin to perfect competition. These findings offer micro-foundations for adopting visibility-restriction technologies (e.g. Zero-Knowledge Proofs) and guide the design of context-specific BCT systems. By reconciling transparency-privacy tensions and demonstrating BCT's endogenous role in forecasting, this study advances strategies for enhancing supply chain resilience through BCT innovation.
N. Mohankumar, V. Sindhu, N. Nageswari, N. Silambarasan
Safe and transparent e-voting is becoming more and more important in modern democracies, as the confidence of citizens in electoral systems is determined by the issues of trust, privacy and scalability. Existing e-voting systems, however, have privacy, impersonation vulnerability, lack of transparency, and coercive weaknesses, and so they must be improved through cryptographic and identity solutions. In an attempt to provide security at these points, to propose a voting system that uses Aadhaar-linked decentralized identities together with iris scan biometrics to authenticate voters, zk-SNARKs to produce zero-knowledge proofs of voter eligibility without revealing their personal data, and homomorphic encryption to ensure ballot confidentiality and allow vote counting to be verifiably processed. Moreover, coercion resistance is ensured by a revoting mechanism, as only the last authenticated vote is included in the counting, thereby mitigating external pressure or vote-buying. The results demonstrate that the proposed design is capable to concurrently deliver strong authentication, biometric-based impersonation resistance, privacy preservation, end-to-end verifiability, and scalability in e-voting. In general, this framework eliminates major weaknesses of the old systems in addition to increasing voter confidence and integrity of the elections. The integration of decentralized identity, biometric iris recognition, and modern cryptography allows the model to provide a secure, transparent, and non-coercible framework of next-generation democratization procedures in India and can present an open-source, globally replicable solution with large-scale elections.
Internet Traffic Analysis and Secure E-voting
Benford’s Law and Fraud Detection
Advanced Steganography and Watermarking Techniques