Oscar Revelo Sánchez, Alexander Barón Salazar, Manuel Bolaños González
This systematic review examines recent advances in blockchain-based electronic voting systems, motivated by the need for more transparent, secure, and verifiable electoral processes. The rapid growth of research between 2022 and 2025 highlights blockchain as a promising foundation for addressing long-standing challenges of integrity, anonymity, and trust in digital elections, particularly in academic contexts where pilot deployments are more feasible. The review followed PRISMA 2020 guidelines and applied the evidence-based methodology proposed by Kitchenham & Charters. Searches were conducted in six major databases, yielding 861 records; after removing duplicates and applying eligibility criteria, 338 studies were retained. Data were extracted using a structured template and synthesised qualitatively due to the conceptual and methodological heterogeneity of the evidence. The included studies reveal significant progress in blockchain architectures, smart contracts, and advanced cryptographic mechanisms—such as blind signatures, zero-knowledge proofs, and homomorphic encryption. Multiple authentication and verification strategies were identified; however, real-world validations remain limited and largely confined to small-scale academic pilots. Overall, blockchain-based voting systems demonstrate conceptual advantages over traditional and conventional electronic models, especially regarding transparency and auditability. Nevertheless, the field requires stronger empirical evaluation, greater scalability, and clearer regulatory alignment to support broader institutional adoption.
Verifiable and transparent voting must protect democratic process from being interfered or falsified in any form, but traditionally implemented voting systems in electronics aren’t transparent, vulnerable to cheating attacks, and centralized in control. To overcome these problems, an election voting system based on blockchain, embedding cryptography security as well as distributed transparency, was conceptualized. With Ethereum-based smart contracts, Advanced Encryption Standard – Galois/Counter Mode (AES-GCM) encryption maintains secrecy of ballots intact, and integrity and tamper protection through hashing by Keccak-256. The voter registration involved Elliptic Curve Cryptography (ECC) based key generation, and an election time commit reveal scheme to maintain privacy intact and allow for non repudiation. Backend was implemented in Flask and MySQL as database management, and frontend in Streamlit to keep it user friendly and easily accessible during voting hours. Every and each voting in blockchain transactions traceable and checkable to maintain voter privacy intact, thereby providing for auditability and transparency. The architecture also offers for security features to withstand replay attacks, instances of double voting, and data breach, thereby making it dependable and scalable in future polls in democracies.
This study presents HoloCyberChain, an entropy-driven blockchain framework for decentralized cyber-threat intelligence with formal verification and privacy preservation. Each cyber event is encoded as a four-dimensional entropy fingerprint capturing structural, temporal, behavioral, and propagation uncertainty. A novel Shannon–β hybrid distance integrates residual-entropy geometry with β-divergence-based distributional separation, yielding a unified statistical–topological measure of threat dissimilarity. Residuals are transformed into calibrated novelty probabilities through a logistic uniqueness gate, while a proof-of-detection consensus protocol enables publicly verifiable and Byzantine-resilient acceptance of novel intelligence. Privacy is maintained using zero-knowledge entropy proofs, and accepted threats are organized into a spectral threat-intelligence graph that preserves family-level separability. Simulation experiments demonstrate reliable discrimination (ROC-AUC ≈0.81, PR-AUC ≈0.77) and stable calibration under noise and concept drift. Real-world validation using the CICIDS-2017 dataset (225 745 flows, 79 features; 97 718 benign and 128 027 DDoS flows) confirms that DDoS traffic exhibits higher Shannon–β entropy, with right-shifted density profiles, higher medians, and tighter interquartile ranges relative to benign traffic, indicating that the proposed entropy formulation preserves separability under realistic traffic imbalance. These empirical results align with theoretical guarantees and simulation findings, establishing HoloCyberChain as a reproducible, entropy-verified foundation for scalable and privacy-preserving cyber-threat intelligence sharing.
In digital security, anonymous credential systems are essential to ensure secure and private interactions. These systems have practical applications in various fields, such as online voting, healthcare, and financial services. However, due to high computational overhead and complex architecture, traditional anonymous credential systems often suffer from efficiency and scalability issues. To address these challenges, we propose an innovative approach that combines advanced cryptographic techniques such as randomized BLS aggregate signatures and optimized zero-knowledge proof usage mechanisms to achieve secure and private identity authentication with minimal overhead. We introduce HPPCS (High-Performance Privacy-Preserving Credential System), an anonymous credential framework that leverages randomizable aggregate signature technology to achieve efficiency and strong security. We conducted a security and experimental analysis of the HPPCS framework, and the results showed that HPPCS improves the efficiency of credential generation and verification while ensuring original security. This work establishes a powerful and practical framework for privacy-centric identity authentication systems.
The rapid adoption of blockchain technology and digital communication platforms has revolutionized financial systems and information exchange. While these innovations promote decentralized trading and global connectivity, they also create fertile ground for malicious activities, including financial fraud and privacy violations. This thesis analyzes these emerging threats and evaluates mitigation strategies, focusing on market manipulation in Decentralized Finance, such as rug pulls and Pump-and-Dump schemes, and the challenges of detecting invasive tracking pixels in email ecosystems.
For the past three decades, the architecture of the internet has rested on two primary pillars - communication on the World Wide Web and Value such as Bitcoin/Distributed ledgers. However, a third critical pillar, Private Coordination has remained dependent on centralised intermediaries, effectively creating a surveillance architecture by default. This paper introduces the 'Stateless Pattern', a novel network topology that replaces the traditional 'Fortress' security model (database-centric) with a 'Mist' model (ephemeral relays). By utilising client-side cryptography and self-destructing server instances, we demonstrate a protocol where the server acts as a blind medium rather than a custodian of state. We present empirical data from a live deployment (https://signingroom.io), analysing over 1,900 requests and cache-hit ratios to validate the system's 'Zero-Knowledge' properties and institutional utility. The findings suggest that digital privacy can be commoditised as a utility, technically enforcing specific articles of the universal declaration of human rights not through policy, but through physics.
Current electronic voting infrastructure continues to be plagued by security, transparency and voter privacy concerns, in both large and remote elections. In order to cope with these issues, this paper introduces a blockchain-based voting system which fulfills end-to-end verifiability and maintains ballot secrecy. The system proposed uses a permissioned blockchain system along with Byzantine Fault Tolerant (BFT) consensus protocol to guarantee data integrity and resilience to faults when facing partially adversarial conditions. The combination of homomorphic encryption of tallying encrypted votes and zero-knowledge proofs of voter eligibility and validation of ballots, without disclosing the content of the vote, results in vote confidentiality and auditability. Smart contracts facilitate the process of vote validation and aggregation making it publically auditable without trusting third parties. Simulated workload performance evaluation suggests that, under regular operating conditions, the system has a verification accuracy greater than 98 % and has an average processing time and computational overhead that are lower than those of the corresponding blockchain-based voting systems under realistic operating conditions. The given framework is planned to assist with the real-time auditing and ensure privacy assurances. Besides this, the paper also addresses the practicability of post-quantum cryptographic primitives and cross-chain mechanisms as further improvements to ensure enhancement of long-term security and scalability.
Rahul Aravindh M, Prasannavelan R M, Pradeep N, K. Malathi
A secure and transparent blockchain-based voting system is proposed, designed to preserve voter anonymity, prevent tampering, and ensure one-vote-per-user compliance in decentralized digital elections. The system introduces a lightweight voter authentication layer using one-time password (OTP) verification, with off-chain hashed identity storage to prevent exposure of personal data. Unlike traditional models that rely solely on smart contract logic, this approach strengthens the end-to-end security boundary by validating user eligibility before on-chain interaction. Votes are cast through smart contracts deployed on a public blockchain, ensuring immutability and auditability, while maintaining voter anonymity by detaching authentication logic from vote recording. To address performance bottlenecks and storage limitations, non-critical identity data is excluded from the blockchain, with hashed authentication tokens acting as cryptographic proofs of voter legitimacy. The proposed method was validated through simulation of small-scale voting rounds, demonstrating secure vote casting with a rejection rate of 100% for duplicate or invalid attempts. Average authentication time remained under 200 milliseconds per session. The modular design facilitates integration with government or institutional ID systems and supports anonymous and verified voting modes, making it adaptable for educational, corporate, or civic deployment. Future iterations will explore zero-knowledge proofs to further enhance privacy guarantees while preserving voter eligibility validation.
As of January 2026, cryptocurrency represents the triumph of marketing over utility. As the FTC and SEC give wide latitude to crypto platforms, can Web3 save crypto from becoming yet another consumer fraud?
ABSTRACT Over the years, numerous efforts have been undertaken to accurately forecast traffic conditions and thereby preventing additional congestion. However, existing crowd management techniques focus on recognizing and counting the crowd while leaving the security of crowd information. A typical crowd management system is centralized and faces challenges, such as contributor selection reliability, fair payment evaluation, privacy concerns and high deployment costs. This study investigates security concerns in crowd management and evaluates the potential of blockchain technology to improve crowd management security. Combining the power of blockchain (decentralization and security) and smart contracts, this work proposes a secure crowd management architecture named . The framework operates on blockchain, utilizes cryptographic algorithms, and incorporates reputation management along with credit distribution through smart contracts. effectively safeguards crowd data while its revenue structure entices users to actively contribute to the system. has been simulated on GoQuorum's Ethereum private blockchain, using elliptic curve signatures for secure and efficient processing. Its performance was tested with RAFT, PoA and IBFT consensus mechanisms where RAFT led in throughput, IBFT lagged and PoA offered a middle ground. PoA stands out for balancing scalability and security, supporting network growth while preserving identity‐based validation and data integrity.
Bitcoin custody systems are constructed under conditions of full knowledge and activated under conditions of partial knowledge. The person who designs a custody arrangement understands its components, dependencies, and intended operation. The person who later encounters that system—often an executor, trustee, or heir—must interpret and operate it without access to the designer's contextual understanding. This paper defines a descriptive framework for examining Bitcoin custody systems under stress conditions at a point in time, producing reference records for later interpretation. The framework treats examination as observation rather than evaluation: it records what exists, what dependencies connect components, and how the system behaves under modeled stress scenarios. It explicitly excludes advice, recommendations, certification, and adequacy assessment. The framework introduces four modeled outcome states—survives, constrained, blocked, and indeterminate—that describe observed system behavior without normative judgment. It defines stress conditions including owner absence, cognitive unreliability, device loss, institutional delay, and coordination failure. It specifies what reference artifacts examination produces: system snapshots, scenario-bound observations, dependency maps, and assumption registries. The paper addresses how professionals—attorneys, fiduciaries, advisors—can engage with examination records without overstepping interpretive boundaries. It distinguishes what records can establish (what was described, what was modeled, what assumptions applied) from what records cannot establish (adequacy, correctness, future outcomes). The framework is offered as a reference for professional contexts where Bitcoin custody must be understood by parties other than its original designer.
Tato práce se zabývá problematikou šíření transakcí v bitcoinové peer-to-peer síti, jejich monitorováním a měřením doby potřebné na jejich propagaci pro různé kombinace parametrů bitcoinových uzlů a sítě. Cílem této bakalářské práce je pochopit principy a mechanismy používané v bitcoinovém protokolu pro šíření transakcí v síti. Na základě získaných informací je navržen a implementován simulační model, který umožňuje sběr statistických dat o procesu šíření transakcí mezi uzly. Součástí práce je také vytvořený analyzační skript, schopný identifikace uzlů, kterými byly dané transakce vytvořeny.
Bitcoin is considered an anonymous transaction technology. Transactions are not directly linked to real names or physical identities of users. However, each transaction is recorded in the blockchain, which is publicly available and allows anyone to perform detailed analysis. This bachelor thesis deals with the issue of attributing cryptocurrency wallets to specific nodes in the Bitcoin peer-to-peer network. The aim of the thesis is to examine the process of transaction propagation between nodes, identify factors influencing their order and propagation speed, and propose methods that will allow estimating the original node responsible for creating or first sending the transaction. The theoretical part describes the basic mechanisms of transaction propagation in the network and analyzes anonymization and deanonymization techniques. The practical part focuses on the design and implementation of heuristics combining propagation time profiles with topological information about the network. For this purpose, a modular platform was developed in the .NET environment, which enables the analysis of data from the P2P network. The contribution of this work is the combination of theoretical principles of transaction propagation with the practical use of data from a real network and the extension of existing methods for analyzing anonymity in the Bitcoin cryptocurrency environment.
Transaction propagation delay limits the block interval and is one of the main bottlenecks in improving Bitcoin throughput. However, transaction relay in Bitcoin is entirely voluntary, which results in low bandwidth and high transaction propagation delay. Improving relay motivation by introducing incentives can effectively reduce delay, but it still faces challenges such as Sybil attacks during reward allocation, leakage of network layer privacy, and high on-chain/off-chain overhead. Therefore, this paper proposes Txtail, a practical transaction relay incentive scheme for Bitcoin, based on continuously attaching relay evidence representing the relays’ identity and contribution during transaction propagation. We employ a free pricing mechanism based on the game between relays to allocate rewards fairly. We design an order-insensitive relay evidence structure based on aggregate signatures and public key mapping, which reduces off-chain data overhead while alleviating the leakage of relay paths by obfuscating the relay order. We construct a verifiable lottery mechanism based on Merkle tree commitments to reduce the data that needs to be uploaded to the chain. Both theoretical and experimental results show that Txtail reduces the per-hop off-chain overhead and the overall on-chain overhead by 96.6% and 79.8%, respectively, compared with state-of-the-art baselines, while remaining practical for deployment.
This paper presents a Blueprint theoretical-practical method for covert control over a decentralized network like Bitcoin by manipulating official distribution channels and modifying the client software. The attack, termed the "Great Tribulation Attack," transforms legitimate users into functional zombie nodes that validate blocks under hidden rules or preprogrammed transactions without their knowledge. This technique does not rely on the 51% hashing power but on client deception.
With the increased usage of Bitcoin and othercryptocurrencies, there is a need to address issues related tofraud detection in cryptocurrency systems. Such issuesinclude double-spending, money laundering, and accounthacking, among others, that Bitcoin needs to guard against.However, since Bitcoin is decentralised and transactions arenot reversible, the use of central-system approaches cannotbe applied; thus, an alternative approach must be adopted.The presented project offers a viable method of usingmachine learning for Bitcoin fraud detection. The frauddetection method is real-time, using ensemble stacking,which entails combining multiple machine learning modelsto enhance prediction capabilities. Algorithms to be usedinclude Random Forest, Gradient Boosting (XGBoost,LightGBM), Support Vector Machine (SVM), LogisticRegression, and Isolation Forest. In other words, multiplealgorithms will be used to examine Bitcoin transaction data,such as amounts transacted, transaction frequency, andtransaction patterns. Ensemble stacking allows the use of thestrengths of multiple algorithms, while the real-time functionenhances the applicability of the approach. Scalability isanother critical consideration, especially considering thenumber of Bitcoin users. This is why the use of a Flaskapplication server will be necessary for user datasubmissions, visualisation, and sending fraud notifications.Evaluation will be based on accuracy, precision, recall, andF1-score.Conclusion – The proposed solution appears quiteplausible as the fraud detection through machine learning isefficient, while scalability is one of the main features of theapproach.
Democratic governance depends fundamentally on the integrity of electoral processes, as public trust in democratic institutions is only as strong as the systems that underpin them. Conventional voting mechanisms whether paper-based or early digital systems face growing scrutiny regarding transparency, auditability, and resistance to manipulation, with high-profile electoral controversies in recent years intensifying calls for more robust and verifiable alternatives. This paper proposes and evaluates a blockchain-powered electronic voting (e-voting) system designed to overcome these longstanding limitations by leveraging the core properties of distributed ledger technology: immutability, decentralization, and cryptographic verifiability. The proposed architecture integrates Ethereum-based smart contracts for automated ballot management, zero-knowledge proof (ZKP) protocols for voter privacy, and a permissioned blockchain layer for regulatory compliance. Together, these components form a cohesive framework that seeks to balance openness with accountability. A comparative evaluation against existing solutions demonstrates measurable improvements in security, voter anonymity, and system auditability. Scalability constraints, regulatory considerations, and real-world deployment challenges are also examined with candor, acknowledging that no technological solution is without friction. The analysis concludes that blockchain-based e-voting represents a technically viable and socially consequential advancement in democratic infrastructure one with the potential to restore and reinforce public confidence in electoral outcomes worldwide.
Federated learning across IoT devices must simultaneously protect each device’s update from disclosure, prevent malicious participants from biasing the global model, and hide which devices are participating from outside observers. Existing systems typically address only a subset of these goals: secure aggregation hides individual updates but cannot validate them, plaintext-based robust filtering requires the server to see updates, and most cryptographic pipelines ignore timing privacy. This paper presents TriSAFE, a protocol composition for IoT federated learning with a single coordinating server and three threshold helpers. The server holds no decryption key. TriSAFE combines four mechanisms that are usually studied in isolation: (i) encrypted client updates accompanied by zero-knowledge proofs that each coordinate lies within a bounded range; (ii) a new lightweight binding step (the plaintext-equivalence protocol, PEP) that cryptographically ties the values proven in zero knowledge to the exact ciphertext later aggregated by the server, closing a substitution gap left by range proofs alone; (iii) helper-added differential privacy noise applied homomorphically before any decryption, so the server only ever sees a noised aggregate; and (iv) fixed-cadence batching with calibrated cover traffic to hide participation from passive network observers. Across two IoT intrusion-detection benchmarks (Edge-IIoTset and N-BaIoT) and MNIST, TriSAFE keeps accuracy within 0.1-2.1 percentage points of the no-attack baseline under Byzantine, label-flip, FANG, and time-delay attacks, with attack success rate below 1% (<0.1% for FANG). Timing inference by a passive observer drops close to chance, and the end to end overhead is 7-36% relative to a non-defended baseline. On MNIST, TriSAFE achieves 89-91% accuracy, 15-17 points above the MODEL benchmark under the same attack suite. The design is practical for gateway-assisted IoT deployments under the assumption that the coordinator does not collude with two helpers and that at least two helpers contribute honest DP noise.
Siqin Li, Kun He, Min Shi, Yajing Huang · 6 authors
Anonymous submissions inspire people to speak up since hiding their identities can protect them from negative influence by their own words. However, the abuse of anonymity may bring harassment to those public submission callers. Existing works only handle DoS attacks or block harassment senders in an active manner, which behave poorly in the early prevention of uncharacterized harassment. In this paper, we propose MsgFliter, a sender-anonymous messaging system with proactive anti-harassment mechanism. Our core idea is to prevent unanswered senders from sending messages continually while keeping their identities, messages, and sender types secret. To meet the functionality and security requirements of MsgFliter, we propose the Anti-Harassment Anonymous Authentication (AHAA) protocol. We associate messages from the same sender through linkable tags and invalidate linkability when a message is replied to. To achieve session indistinguishability, we further combine the proposed anonymous authentication with zero-knowledge proofs of disjunctive relations. We implement MsgFliter and compare its performance with related solutions. Experimental results show that our solution is practicable.