On August 28, 2026, Google, Microsoft, Anthropic, OpenAI, and 100 other companies signed an open letter warning of a large-scale AI attack. AI has created systemic risks in the digital world, but the physical world has no defense mechanism. This paper defines the Physical Ledger—a physical world namespace rooted in the Cui coordinate. The Physical Ledger DNS is not a copy of the domain name system; it is an object-addressing protocol for the physical world: every object (shelf position, robot, door, vehicle, starship) is assigned a unique Cui coordinate address. This paper presents a draft protocol for the Physical Ledger DNS, a catalog of 108 problems, the genesis valuation of $100,000,000, and a reward distribution scheme. It proposes the §13 security mechanism (Proof-of-Problem): a distributed firewall for the Physical Ledger DNS, powered by the 108 problems. The more solvers participate, the thicker the firewall. AI can attack digital protocols, but it cannot solve problems—because solving requires understanding the coordinate origin itself. The genesis valuation of the Cui-attribute Shell is defined as US$100,000,000, anchored at 2026-08-27. The appendix includes the Cui-coordinate naming rights and the passphrase lock (recognition of 1/7/8 for entry).
Henry Ohiani Ohize, Adeiza James Onumanyi, Lukman Adewale Ajao, Buhari Ugbede Umar · 9 authors
Despite significant advances in electronic voting technologies, voter accreditation in many electoral systems remains vulnerable to identity fraud, database tampering, equipment failure, and centralized security breaches. Existing accreditation solutions often rely on single-modal biometric authentication and centralized architectures, limiting their robustness, transparency, and public trust. This paper proposes a Blockchain-based Bimodal Voter Accreditation System (Block-BVAS), together with a practical framework for its deployment in electronic voting systems. The proposed system integrates multimodal biometric authentication using facial and fingerprint recognition with a private Ethereum blockchain and conventional cryptographic mechanisms to provide secure, tamper-resistant, and auditable voter accreditation to provide secure, decentralized, and tamper-resistant voter accreditation. A Raspberry Pi 5 serves as the embedded processing platform, demonstrating the feasibility of implementing the framework on cost-effective hardware. By combining distributed-ledger technology with encrypted biometric verification, the proposed architecture enhances the integrity, confidentiality, and immutability of election-related records while addressing limitations associated with single-factor authentication and conventional centralized record management. Experimental evaluation of the biometric authentication module performed effectively, with fingerprint recognition achieving an average authentication accuracy (AA) of 97.8% and facial recognition averaging 95.1%. The blockchain storage overhead (BSO) displayed a near-linear growth pattern relative to the number of transactions, consistent with theoretical expectations for blockchain architectures. Reliability analysis indicated system uptime exceeding 95%, with only minimal operational failures recorded during the test period. This blockchain implementation further demonstrated reliable transaction processing and secure record management, indicating the effectiveness of the proposed Block-BVAS in enhancing the security, transparency, and trustworthiness of electronic voter accreditation.
Open access
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
The classical taxonomy of remote voting security requirements is organized into two levels: basic and counter-attack requirements. This classification has remained largely unchanged over a decade, even through scheme innovations such as verifiable re-voting, tally-hiding, and post-quantum protocols. However, this stability does not survive the new threat model of adversarial artificial intelligence (AI). A security requirement is considered promoted when AI raises the adversarial grade at which it must be defended above its original assumption. In this paper, we introduce requirement promotion as a framework for re-evaluating the classical taxonomy, contending that the destabilizing factor is not new cryptography but the emergence of a new adversary. We analyze five requirements under both AI-amplified threats and AI-enabled defenses, demonstrating that promotion fires selectively. Basic requirements such as privacy, fairness, and eligibility undergo tier promotion to counter-attack grade, driven by machine-learning-based deanonymization, pre-tally outcome inference, and synthetic-identity fraud. Incoercibility undergoes supra-tier promotion, surpassing the existing counter-attack toolkit, as deepfake-generated coercion evidence compromises the fake-credential assumptions of classical coercion-resistant schemes. Verifiability, where AI-based defense is robust, resists headline promotion but acquires a new sub-requirement at its seam with software independence: verifying the opaque machine-learning components in the audit pipeline itself. We then propose a reference architecture, integrating existing primitives such as lattice-based zero-knowledge proofs, deniable re-voting, statistical election forensics, and time-lock decryption into a layered design that addresses the promoted requirements, with explicit analysis of residual gaps.
Mrs. A. Asrin Mahmootha, B. Aysha Banu, Mohammed Muhajir S, Mohamed Anas A · 5 authors
Voting is a central component of a country’s political life cycle. Privacy, authentication, and integrity of citizens’ votes are essential requirements of any electronic voting programme. To address these concerns, this paper proposes a hybrid e-voting system that integrates personal and public blockchain with a machine learning–based intrusion detection mechanism. The personal blockchain governs voter registration and vote casting, while the public blockchain stores the Merkle root hash for result integrity verification. An ML-based intrusion detection system monitors voting data centres and e-voting stations for anomalous behaviour. Homomorphic encryption and zero-knowledge proofs preserve voter anonymity. Experimental evaluation demonstrates that the proposed framework achieves an accuracy of 97.4%, precision of 96.8%, recall of 97.1%, and F1-score of 96.9% in detecting intrusion attempts. The system also reduces transaction latency by 34% compared to conventional blockchain voting systems. Results confirm that the framework delivers transparency, tamper-resistance, and strong security guarantees, making it a viable solution for modern democratic elections
The article discusses a decentralized electronic voting system based on blockchain technology. This study aims to improve the performance and fault tolerance of blockchain-based electronic voting systems by introducing the Automated Leaderless Byzantine Fault Tolerance (AL-BFT) consensus protocol. This study aims to develop and evaluate an electronic voting system model that applies the proposed AL-BFT consensus mechanism in a permissioned peer-to-peer network. The methods used include computer modeling of a peer-to-peer (P2P) network, implementation of a decentralized ledger, and experimental load testing of the consensus protocol. System performance is evaluated using key metrics, such as transaction latency, throughput (requests per second), fault tolerance threshold, and scalability. The study results include the development of a conceptual architecture for the electronic voting system, the identification of its core components, and the analysis of their interactions to ensure data integrity and the reliability of voting results. At each stage of the electoral process, data security is considered, and additional protection mechanisms are analyzed to enhance system robustness. Eliminating the leader election phase from the consensus process is a key feature of the proposed approach, thereby reducing coordination overhead and enabling more efficient agreement among nodes. The proposed AL-BFT protocol reduces transaction latency and improves throughput while maintaining the fault tolerance level of traditional Byzantine Fault Tolerance-based approaches. The results confirm improved efficiency compared to classical leader-based consensus mechanisms, particularly in small permissioned blockchain networks. Conclusions. A practical implementation of the system has been developed and tested under real simulated load conditions. The proposed solution ensures stable system operation and reliable consensus formation. The system can be effectively applied to university elections, organizational voting, and other scenarios that require transparency, security, and manipulation resistance
Novan Ilham Ramadhan, Rizky Parlika, Ardhon Rakhmadi
Electronic voting (e-voting) systems continue to face challenges related to transparency, result validation, and duplicate voting prevention. Blockchain technology offers characteristics such as decentralization, transparency, and immutability that can support more auditable voting processes. This study presents a prototype implementation of a blockchain-based e-voting system using Ethereum smart contracts to support transparent vote recording, public auditability, and wallet-level double voting prevention. The system was implemented using Solidity-based smart contracts deployed on the Ethereum Sepolia Testnet and integrated with MetaMask for transaction authentication. Smart contracts manage election creation, candidate registration, voter registration, vote recording, duplicate vote prevention, and voting result finalization. An audit trail mechanism was implemented to allow voting activities and transaction records to be publicly verified through the Etherscan blockchain explorer. All predefined functional testing scenarios were executed successfully. The evaluation results indicate an average gas usage of 75,109 gas, an estimated transaction cost of 0.000113 ETH, and a transaction latency of approximately 4–5 seconds. The implemented wallet validation mechanism and hasVoted mapping effectively prevent duplicate voting attempts associated with the same wallet address. However, the proposed system represents a prototype-level evaluation conducted on the Ethereum Sepolia Testnet and does not provide voter identity verification, ballot anonymity, or real-world election readiness. The findings demonstrate the feasibility of Ethereum smart contracts for transparent auditability and wallet-level double voting prevention in blockchain-based voting environments.
Online voting platforms that rely on classical cryptography and centralized trust anchors face escalating challenges as the demand for secure and transparent digital elections grows. Such systems remain exposed to quantum-era threats, insider manipulation, and delayed audit mechanisms, which together can undermine public confidence and electoral legitimacy. To counter these risks, a quantum-resistant, multi-layer blockchain architecture has been developed to enable remote voting with continuous verifiability and resilience. This architecture resolves key weaknesses through five integrated layers. Quantum-Resistant Distributed Ledger Initialization (QR-DLI) embeds lattice-based cryptography, specifically Kyber and Dilithium variants, directly within the genesis block, ensuring the ledger is tamper-proof from inception and immune to quantum brute-force attacks. The Self-Adaptive Smart Contract Governance Engine (SASCG) introduces dynamic, participation-aware rule adjustments, allowing principled governance without manual overrides and ensuring that voting periods and eligibility rules adapt securely in real time. Homomorphic Vote Encryption with Multi-Authority Shard Key Distribution (HVE-MASKD) guarantees ballot confidentiality and authenticity by combining fully homomorphic encryption with distributed key shares, eliminating single points of trust. The Zero-Knowledge Proof–Based Real-Time Audit Layer (ZKP-RTAL) continuously validates ballot integrity while concealing vote content, creating a public and immutable audit trail. Finally, the Federated Performance & Threat Intelligence Optimizer (FPTIO) aggregates live telemetry and historical attack data to proactively tune consensus parameters and predict potential intrusions without interrupting the election process. Collectively, these layers achieve sub-second cryptographic operations, transaction throughput exceeding 1,500 TPS, over 99 % fraud detection accuracy, and strong scalability. The model provides a future-ready, auditable replacement for current e Voting infrastructures, strengthening digital democracy through post-quantum security, adaptive governance, and intelligent, continuous optimizations.
Digital image steganography has evolved from traditional rule-based techniques to advanced data-driven frameworks enabled by deep learning. However, existing surveys remain fragmented, often focusing on limited aspects while overlooking emerging paradigms such as blockchain-integrated and quantum-based approaches. This paper presents a comprehensive and systematic review of digital image steganography following the PRISMA 2020 guidelines, covering studies published between January 2015 and April 2026 across six major scientific databases. From an initial pool of 26,539 records, 83 relevant studies were selected through a rigorous two-stage screening process. The review provides a unified analysis of steganographic techniques by examining five dimensions: structural evolution and taxonomy, algorithmic modifications and hybridisation, application domain mapping, integration of emerging technologies, and future research trends. Comparative evaluation indicates that deep learning-based methods achieve 18–23% higher steganalysis resistance than classical approaches, whereas classical methods retain a 5–8 dB PSNR advantage. The quantitative synthesis further confirms the inherent capacity–imperceptibility–security trilemma, wherein no reviewed technique simultaneously achieves $$\text {PSNR} > 42$$ dB, embedding capacity $$> 4$$ bpp, and detection error rate $$> 0.48$$ . Six open challenges and seven future research directions are identified and grounded in evidence from the included studies, with explainable steganography, quantum-resistant frameworks, and latent diffusion model integration emerging as the most critical priorities for advancing the field toward practical and secure deployment.
Open access
Advanced Steganography and Watermarking Techniques
Abstract Cross-chain bridges have become indispensable in frastructure for asset and data portability across heterogeneous blockchains, yet they remain the single most exploited category of decentralized-finance infrastructure, with cumulative publicly reported losses exceeding two billion US dollars since 2021. Existing literature addresses this problem from two largely disconnected angles: broad interoperability surveys that cat alogue protocol families, and narrower security studies that catalogue historical incidents. Neither strand provides a reusable, transparent instrument that lets a developer or auditor estimate a candidate bridge’s exposure to known attack classes before deployment, using only publicly observable design parameters. This paper addresses that gap. We conduct a PRISMA-guided systematic literature review of blockchain-interoperability and bridge-security research, from which we derive a unified tax onomy cross-tabulating bridge architectures, verification models, communication models, trust assumptions, and attack categories. Building on this taxonomy, we propose the Bridge Security Risk Score (BSRS), a rule-based, auditable scoring framework– deliberately not a machine-learning model– that maps seven publicly inspectable architectural parameters to a quantified risk estimate. We retrospectively validate BSRS against eight publicly documented bridge incidents (Ronin, Wormhole, Poly Network, Nomad, Harmony Horizon, Multichain, ChainSwap, Qubit) and two non-exploited comparators (LayerZero, Axelar), and find that the framework consistently assigns higher risk bands to the exploited designs. We discuss the limitations of retrospective vali dation, threats to validity, and directions for extending this work toward automated, PhD-level formal-verification and disclosure standardization research.
The rising use of the Internet of Things (IoT) has changed the communication and automation landscape in various industries. However, the growing number of interconnected and vulnerable IoT devices has created several cybersecurity challenges, and the conventional intrusion detection system is not designed to handle the dynamicity of sophisticated cyber-attacks and secure information management. This study presents a blockchain-based security framework for intrusion detection in an IoT environment that uses a Gated Recurrent Unit (GRU) to achieve high-level detection accuracy and blockchain technology to guarantee information security. Edge-IIoTset benchmark data containing about 2.2 million traffic instances and 61 traffic features were collected, preprocessed, and split into training, validation, and testing datasets at a ratio of 70:15:15 for model development and evaluation. The GRU network was trained to capture sequential patterns in network traffic with high accuracy, while the blockchain layer was leveraged to ensure secure detection record storage and information sharing. The model attained 99.12% accuracy, 99.08% precision, 98.97% recall, 99.02% F1-score, and 0.9987 ROC-AUC. Additionally, the blockchain layer achieved an average of 850 transactions per second with a 2.3-second block confirmation time, while the framework recorded an average of 3.2 millisecond traffic detection time. Thus, the proposed framework was efficient and effective in detecting and responding to cyber-attacks in an IoT network.
Supervisors need reliable assurance over balances, issuance, and transaction integrity. Institutions need toprotect sensitive financial data. Traditional audit practice often resolves this tension by granting broad accessto ledgers. That approach is effective, but costly in privacy, operational risk, and cross-border data exposure.This note presents a settlement architecture in which compliance statements can be proven cryptographicallywithout disclosing the full ledger. A supervised entity can demonstrate that a balance equals a value, exceeds athreshold, or lies within a band. Verifiers check the proof without receiving account-level books. Spending keysremain on the client side and do not travel to the operator to authorize a transfer.A second confidentiality property is reported that is easy to miss in architectural summaries: in a settlementthat updates both accounts in a single transition, the payer must know the recipient’s balance in order toconstruct the proof. Paying someone therefore reveals what they hold. The architecture addresses this with atwo-phase transfer, at a stated cost in finality latency.The paper is deliberately non-utopian. It specifies which properties become demonstrable and which residualtrust remains—especially in a single-node deployment where the operator may still observe state, sequencetransactions, or censor. The institutional claim is modest: zero-knowledge settlement can reduce routine fullledgerdisclosure while improving the quality of evidence for specific supervisory questions.
Electronic voting has become an important digital governance mechanism for remote elections, institutional decision-making, shareholder voting, public consultations, and large-scale Internet-based democratic participation. Despite its growing relevance, secure electronic voting remains difficult to implement because a practical system must simultaneously preserve voter anonymity, verify voter eligibility, prevent double voting, ensure ballot integrity, support public auditability, and maintain acceptable transaction throughput. To address these challenges, this study proposes a post-quantum secure and privacy-preserving blockchain-based electronic voting framework that integrates Dilithium digital signatures, zero-knowledge proofs, nullifier-based double voting prevention, encrypted ballot submission, smart contract-based election rule enforcement, and a Byzantine fault-tolerant consensus mechanism. In the proposed architecture, Dilithium signatures are used for post-quantum authentication of voter transactions and validator messages, whereas the zero-knowledge proof layer is used separately to verify voter eligibility, candidate validity, credential ownership, and correct nullifier generation without revealing the voter identity or ballot choice. Dilithium verification is performed externally at the transaction authentication layer, while the zero-knowledge circuit handles privacy-preserving voting logic. Each voter locally generates a private credential and submits only a public commitment during registration, thereby reducing the risk of authority-based impersonation or identity-to-vote linkage. Smart contracts verify the proof, reject reused nullifiers, enforce voting rules, and record auditable election events on the permissioned blockchain ledger. Experimental evaluation demonstrates that the proposed prototype achieves an average throughput of 408 transactions per second and an average block finalization time of 2.18 s under stress testing. The results indicate that the framework can provide a practical balance between post-quantum security, privacy preservation, verifiability, and transaction efficiency in permissioned electronic voting environments.
The pseudonymous nature of blockchain transactions, combined with the rise of encrypted DNS protocols such as DNS-over-HTTPS (DoH) and DNS-over-TLS (DoT), has created a new frontier for sophisticated tax evasion. Malicious actors can now exfiltrate transaction details and coordinate transfers by encoding data within the payloads of encrypted DNS queries, effectively bypassing traditional network monitoring and forensic analysis. This paper proposes a novel detection framework that leverages a hybrid deep learning architecture to identify such covert, tax-evading activities. Our system integrates a Convolutional Neural Network (CNN) for its superior ability to extract spatial and sequential patterns from raw network flow data and encrypted payload characteristics, with a Long Short-Term Memory (LSTM) network to model the temporal dynamics of blockchain interactions and DNS query sequences. By fusing these two paradigms, the hybrid model can distinguish between benign encrypted DNS traffic and malicious payloads used for illicit financial coordination. We evaluate our framework using a synthetically generated dataset that simulates realistic tax-evasion strategies, including micro-transaction splitting and delayed transaction relaying. Preliminary results indicate that our approach achieves a significantly higher detection rate and lower false-positive rate compared to conventional signature-based or single-model machine learning methods. This research demonstrates the efficacy of hybrid neural networks in preserving financial integrity and provides a critical tool for regulatory agencies to enforce tax compliance in the age of encrypted communications and decentralized finance.
The transition from traditional paper-based voting to electronic systems has introduced significant efficiencies but has simultaneously created centralized vulnerabilities, including susceptibility to database manipulation and a lack of transparent audit trails. This research proposes a decentralized, blockchain-based voting framework designed to restore public trust through cryptographic immutability and end-to-end verifiability. By utilizing a Permissioned Proof of Stake (PPoS) consensus mechanism, the system achieves the high transaction throughput necessary for national-scale elections while maintaining a decentralized security posture that prevents any single entity from compromising the results. The technical core of this framework integrates Zero-Knowledge Proofs (ZKPs) to resolve the tension between voter anonymity and auditability. This allows voters to prove their eligibility and the validity of their ballot without disclosing their identity or specific choice, thereby upholding the sanctity of the secret ballot. To address modern security threats, the study incorporates Post-Quantum Cryptography (PQC) to safeguard against future decryption capabilities and utilizes Layer 2 scaling solutions to ensure network resilience during peak voting periods. Methodological validation was conducted through a simulated electoral environment, testing the system against common attack vectors such as DDoS and 51% attacks. The results indicate that the decentralized model significantly reduces the risk of systemic fraud compared to centralized alternatives. This paper concludes that while socio-technical barriers to entry exist, the proposed blockchain architecture provides a scalable, secure, and transparent foundation for the future of digital democracy.
Yoon-Nyoung Jung, Subin Jo, Seo-Hyun Yun, Hwajeong Seo
Electronic voting systems inherently encompass a structural tension among ballot secrecy, verifiability, and coercion resistance. Voters must be able to verify whether their votes have been included; however, if such verification information can serve as evidence presentable to a third party, it becomes a basis for post-election intimidation. Existing studies have focused primarily on performance evaluation or data separation, and have not comprehensively addressed the structural tension between verifiability and coercion resistance. This study defines this tension as the verification paradox and designs and implements an electronic voting prototype on a three-organization consortium based on Hyperledger Fabric 2.5, combining a 2-of-3 endorsement policy, nullifier-based anonymity, Exponential ElGamal homomorphic tallying, zero-knowledge proof (ZKP)-based ballot validity verification, panic-password-based deniable verification, and Private Data Collection (PDC)-based coerced vote separation. Quantitative evaluation results confirm a server latency overhead of +0.9% for ElGamal relative to the AES performance baseline, statistical indistinguishability between normal and panic responses (p>0.05), and a peak throughput of approximately 40.7 TPS (with an error rate of 0%) under 1000 concurrent voters. Through this prototype implementation and quantitative evaluation, we show the potential of permissioned blockchains to partially and practically mitigate the verification paradox. This study, however, does not provide a formal security proof, and it is subject to a trust assumption on PDC as well as to the experimental limitations of a single evaluation environment and a limited load range.
ASEGUNLOLUWA E. BABALOLA, DAVID O. ILESANMI, PREYE ADEOLA
Electronic voting can improve the speed of ballot processing and result generation, but conventional systems often depend on centrally controlled infrastructure that may create concerns relating to record alteration, transparency and administrative control. This study presents the development of a blockchain based electronic voting prototype that integrates election creation, candidate management, voter address authorization, ballot submission and result retrieval within a web application. The system adopts an Ethereum based architecture comprising a Next.js user interface, Web3 communication, MetaMask wallet connection, Solidity smart contracts and a local blockchain environment provided by Ganache. A factory smart contract is used to create separate election contracts, enabling each election to maintain its own candidates, authorized voter addresses, election status and vote totals. Before a ballot is accepted, the relevant election contract verifies that the election is active, that the submitting address is authorized and that the address has not previously voted. The developed prototype provides interfaces for election creation, voting and result presentation, demonstrating the integration of the web application with the smart contract and blockchain components. The study provides a basis for the independent management of multiple elections through separate smart contract instances.
Permissioned ledgers are commonly treated as centralised because admission is restricted. This paper separates permissioning from control distribution and proposes identity-staked consensus as a trust model for accountable settlement ledgers operated by chartered validators. The model treats validator identity as externally costly collateral: public legal identity, charter state, institutional reputation, liability, attributable audit exposure, a phase-indexed conditional identity-loss floor, and loss-realisation channels outside the protocol. It distinguishes this construct from proof-of-authority by formalising validator acts as actor constellations, public validator anchoring, affiliation-aware voting caps, threshold class coverage, per-member collusion margins, observer-supported detectability, bootstrap claim discipline, and a consensus/application enforcement boundary. The paper connects the model to a broader identity-infrastructure series: the actor-assurance paper supplies capability-gate evidence, the trust-anchor paper supplies public validator anchoring and assurance-at-time, and the delegated-authority paper can consume the ledger evidence record for mandate and model-attribution records.
Inundating networks with traffic to cripple service availability defines a DDoS attack. Traditional defences, like firewalls and centralized scrubbing centers, can suffer from single points of failure during large-scale attacks. Enter new blockchain technology, with Ethereum probably leading the way in decentralized solutions. Since Ethereum smart contracts enable DDoS detection and enforcement of validation rules, events can be managed automatically, applying rewards or penalties without a central authority. In this way it leverages the positive aspects of crypto-economic mechanisms and reputation systems; giving people an incentive to honestly participate while making abusing the system unprofitable, creating a trustless, transparent, resilient decentralized defence against cyber threats. The architecture of a blockchain-validated system that gates access to services includes a back-end gateway responsible for verifying transactions on the blockchain before processing user queries. The off-chain detection algorithm identifies unexpected traffic spikes that exceed a predefined threshold. Attackers were deterred by cost, the system itself stayed up and running, and the smart contract worked autonomously. The suggested approach maintained 96% access success for authorized users, successfully blocked 92% of DDoS traffic, and guaranteed 98% uptime during simulated attacks. All validated access attempts were 100% immutably recorded on-chain, and attackers had to pay 300% more employs transparent on-chain rules and Ethereum smart contracts to manage access. Because every access attempt is permanently documented on the blockchain, it is difficult to alter logs or stop denial-of-service attacks without detection.
Kanan Huseynov, Ali Shahzaib, István András Seres, János Tapolcai
From a user's perspective, perhaps the most significant difference between traditional banking services and widely used blockchain-based financial systems is that, in the latter, transactions and, either directly or indirectly, account balances and transaction histories are publicly observable. Therefore, a growing number of cryptographic solutions have been proposed to add a privacy layer to such systems. However, the privacy that users actually obtain does not depend solely on the security of the underlying cryptographic protocol: user behavior, transaction amount patterns, and timing decisions can substantially reduce anonymity. In this work, we study behavioral leakage in cryptocurrency mixers, focusing on Railgun on Ethereum. We aim to heuristically estimate the probability that a given deposit and withdrawal transaction belong to the same user. We consider five sources of leakage: characteristic timing patterns, address reuse, proximity in the transaction graph induced by prior public transactions, amount fingerprints that preserve distinctive digit patterns across transaction values, and knapsack type matches in which groups of transaction amounts add up in revealing ways. Our results show that even cryptographically strong privacy systems may suffer substantial anonymity loss due to user behavior and transaction patterns. Our five heuristics are able to uniquely link 17.65% of Railgun withdraw transactions to deposit transactions. We also applied a knapsack solver algorithm that was able to produce a 3.42 bit median anonymity loss for withdraw transactions. This work contributes to a better understanding of the practical privacy limits of mixers and anonymity pools, and points toward safer usage practices and design principles.
This paper presents a blockchain-based electronic voting system designed to address the persistent challenges of transparency, security, and integrity in democratic electoral processes. Traditional voting systems in countries like Nepal suffer from vote manipulation, ballot rigging, logistical inefficiencies, and limited public trust. To overcome these limitations, this work proposes a decentralized e-voting application built on the Ethereum blockchain, leveraging smart contracts for tamper-proof vote recording and enforcement of voting rules. The system incorporates multi-factor authentication, combining facial recognition via OpenCV with Voter ID and Date of Birth verification to ensure only eligible voters participate. MetaMask wallet integration enables secure blockchain transactions, while Web3.js facilitates real-time interaction between the frontend and the deployed smart contracts on Ganache. The methodology encompasses data collection, voter authentication, smart contract deployment, and result retrieval. This paper offers a scalable and cost-effective alternative to conventional voting methods, with future scope for public Ethereum deployment and expanded biometric authentication.
Democratic elections rely on trust, transparency, and tamper-resistance -- qualities that conventional and early electronic voting systems have consistently failed to guarantee. This paper presents a Blockchain-Enabled Secure E-Voting Framework with Facial Recognition for Voter Authentication, designed to address persistent vulnerabilities in existing electoral systems. The proposed system integrates a permissioned blockchain ledger with deep-learning-based facial biometric verification to ensure decentralized, immutable vote storage and strong identity assurance. A multi-layer security architecture combines homomorphic encryption, zero-knowledge proofs, and digital signatures to preserve voter anonymity while enabling end-to-end verifiability. Anti-spoofing and liveness detection mechanisms prevent impersonation via photographs, video replays, or deepfake-generated imagery. Smart contracts automate vote counting and result publication, eliminating human involvement in the tallying process. Experimental evaluation demonstrates a facial recognition authentication accuracy of 97.3% and an end-to-end voting transaction latency under 500 milliseconds, with blockchain confirmation averaging 2.4 seconds. The framework is scalable to national-scale elections and applicable to governmental, corporate, and institutional governance contexts.
This dissertation mostly focuses on a block-chain based voting systems. It aims at identifying the strategies and the guidelines as well as provides a comprehensive end-to-end electronic voting system based on block-chain, with the help of encryption private and public techniques such as zero-knowledge proofs to improve privacy. The proposed method is to provide Security and Privacy, Transparency and Trust, Accessibility, Efficiency and Convenience to voters and Security and Fraud Prevention in Election Outcomes. The proposed online voting system using cloud-based hybrid block-chain technology eradicates the flaws that persist in the existing voting system, and it is carried out in three phases: The registration phase, Vote casting phase and Vote counting phase. The integration of SHA-512 encryption ensures that the voter's identity remains confidential and the integrity of the vote is maintained. The system also demonstrates improved security, reduced operational cost, real-time vote counting, and transparency for both voters and election authorities. The proposed method shows better result as compare to other previous method in terms of different result parameters such as block size, encryption, decryption, Block size (64 bits), Word size, hash output and number of Rounds. In the nut shall the proposed method perform better due to SHA 512 technique and provide better security as compare to other methods.