SecureVote: Enhancing Electoral Integrity through Blockchain and Biometric Multi- Factor Authentication

SecureVote: Enhancing Electoral Integrity through Blockchain and Biometric Multi- Factor Authentication

Alinsha S

B.Tech Student, Dept. of Computer Science and Engineering College of Engineering, Kottarakkara Kottarakkara, India

Fahad A

B.Tech Student, Dept. of Computer Science and Engineering College of Engineering, Kottarakkara Kottarakkara, India

Althaf K A

B.Tech Student, Dept. of Computer Science and Engineering College of Engineering, Kottarakkara Kottarakkara, India

Shruthi M Pillai

Assistant Professor, Dept. of Computer Science and Engineering College of Engineering, Kottarakkara Kottarakkara, India

Chris P Reji

B.Tech Student, Dept. of Computer Science and Engineering College of Engineering, Kottarakkara Kottarakkara, India

Dr. Shani Raj

Associate Professor, Dept. of Computer Science and Engineering College of Engineering, Kottarakkara Kottarakkara, India

Abstract – Electronic voting techniques have gained popularity as a contemporary alternative to traditional paper-based elections

because of their effectiveness and accessibility. The current elec- tronic voting methods, however, have signicant security aws, such as multiple voting, identity theft, centralized control, and a lack of transparency. Despite the fact that blockchain technology is decentralized, immutable, and auditable, many blockchain- based voting systems merely employ cryptographic credentials and lack robust voter identication verication processes.

The blockchain-based electronic voting system SecureVote, which incorporates multi-factor verication and facial biomet- ric authentication, is proposed in this study. Ethereum smart contracts are used by the system to guarantee transparent result calculation and tamper-proof vote storage. SecureVote employs one-time password (OTP) validation as a secondary authentica- tion method in conjunction with client-side facial recognition and deep learning-based feature extraction.

The suggested design makes use of Web3.js and a decentralized application (DApp) concept for safe wallet-based transaction signing and blockchain interaction. High authentication reliability, avoidance of double voting, and effective transaction processing with low gas overhead are all demonstrated by the experimental results. SecureVote combines biometric multi- factor authentication with blockchain immutability to enhance the reliability, transparency, and integrity of remote voting.

Index TermsBlockchain Technology, Biometric Identity Verication, Decentralized Ledger, Electronic Voting System,

Ethereum Smart Contracts, Facial Recognition, Multi-Factor Authentication, Secure Remote Voting

1. Introduction

   The credibility of an electoral system directly affects public trust in governance, and free and fair elections are essential to any democratic society. While traditional paper-based voting methods have been in use for decades, they are often linked to issues like ballot tampering, slow counting procedures, logistical complexity, and the potential for human error. These issues have prompted the development of electronic voting (e-voting) systems, which are intended to increase efciency, accessibility, and result accuracy. While e-voting systems streamline the vote casting and counting process, many of their current implementations rely on centralized server archi- tectures. Critical security issues brought about by centralized systems include insider threats, database manipulation, sin- gle points of failure, and unauthorized access. Furthermore, traditional identication methods like voter credentials, ID cards, and passwords could not offer enough defense against identity theft and fraudulent voting. Therefore, maintaining voter authenticity and vote integrity in digital election systems continues to be a signicant concern. Critical security issues brought about by centralized systems include insider threats, database manipulation, single points of failure, and unautho- rized access. Furthermore, traditional identication methods like voter credentials, ID cards, and passwords could not offer enough defense against identity theft and fraudulent voting. Therefore, maintaining voter authenticity and vote integrity in digital election systems continues to be a signicant concern.

   However, many blockchain-based voting ideas prioritize

   secure vote storage over the signicance of trustworthy voter identity verication, even if blockchain offers strong guar- antees for vote immutability and transparency. Voter fraud and illegal access cannot be completely prevented by merely connecting votes to cryptographic keys or digital wallets. Therefore, creating a voting platform that is really secure requires integrating blockchain technology with strong identity identication methods.

   By using distinctive physiological traits like ngerprints, iris patterns, or facial features, biometric authentication provides a potent method of identity verication. Because it is non- intrusive and widely accessible through common camera- enabled devices, facial recognition stands out among these techniques. Biometric systems greatly improve protection against identity fraud and unauthorized voting attempts when combined with multi-factor authentication methods, such as one-time password (OTP) verication.

   In this paper, we present SecureVote, a blockchain-based electronic voting system that combines multi-factor verica- tion and facial biometric authentication. To guarantee tamper- resistant vote storage and avoid double voting, the suggested system makes use of Ethereum smart contracts. Deep learning- based feature extraction is used for facial recognition on the client side, and OTP validation provides an extra degree of security. Because the system is a decentralized application (DApp), Web3.js and wallet-based transaction signing allow for safe blockchain interaction.

   SecureVote seeks to strengthen voter identity assurance, in- crease electoral integrity, and improve transparency in remote voting settings by fusing biometric multi-factor authentication with decentralized ledger technology.

2. Related Work

   In recent years, a lot of research has been done on blockchain-based electronic voting, especially in relation to decentralization, transparency, and integrity. Numerous schol- ars have carried out in-depth analyses looking at the ways distributed ledger technology can enhance election proce- dures. In their thorough technological analysis of blockchain- based electronic voting systems, for example, Berenjestanaki and associates covered cryptographic structures, consensus methods, and system-level security issues. In a similar vein, Ohize and colleagues highlighted scalability constraints and privacy issues in current blockchain voting frameworks while providing a thorough analysis of architectures, trends, and implementation difculties. Although these works provide useful theoretical and architectural insights, they do not present deployable authentication-integrated solutions and are primar- ily review-oriented.

   Abo-Akleek and associates investigated how blockchain, with a focus on tamper resistance and decentralized veri- cation, can improve electoral systems resilience and trans- parency. Pereira and Scott examined whether decentralization could improve citizen empowerment and examined the wider democratic potential of blockchain-based voting. These studies highlight the theoretical advantages of blockchain adoption,

   but the dont concentrate on incorporating robust biometric identity verication systems into real-world applications.

   In a different study, Daraghmi and colleagues showed how blockchain can increase trust and lessen centralized manipula- tion by proposing a decentralized electronic voting architecture designed for particular political contexts. Nonetheless, their frameworks authentication methods are still mostly credential- based. Using the Technology Acceptance Model (TAM), Mannonov and Myeong examined how citizens felt about blockchain-based voting systems and found that usability and trust were important adoption factors. Although their ndings are signicant from a socio-technical standpoint, technical biometric integration is not covered in the work.

   Benabdallah and colleagues, along with Vladucu and co- authors, conducted systematic literature reviews that examined the trade-offs between privacy, integrity, and scalability in blockchain voting systems. The signicance of cryptographic protocols, consensus effectiveness, and anonymity preserva- tion are constantly emphasized in these reviews. However, they stress that identity verication is still a difcult task, especially when it comes to stopping impersonation and illegal voting.

   Gupta and associates presented a blockchain-based voting system that uses structured transaction validation to enhance end-to-end security. Even though their method improves trans- actional security, digital credentials and registration procedures continue to play a major role in authentication. In a similar vein, Kusi and Asoma looked at privacy, integrity, and scal- ability issues in various blockchain voting systems and came to the conclusion that although blockchain greatly enhances vote immutability, useful identity assurance techniques are frequently lacking.

   It is clear from the literature currently in publication that signicant advancements have been made in using decentral- ized ledgers to increase transparency and secure vote storage. Nonetheless, the majority of earlier research focuses on either privacy protection, ledger security, or conceptual architecture design. There is still a lack of research on how to incorporate biometric authenticationspecically, facial recognition and multi-factor vericationinto a fully deployable blockchain- based voting system.

   The suggested SecureVote framework combines OTP-based multi-factor verication, facial biometric authentication, and Ethereum-based smart contract enforcement to close this research gap. SecureVote improves voter identity assurance while preserving decentralization and vote immutability, in contrast to current methods that only use cryptographic cre- dentials.

3. Proposed System

   The SecureVote framework combines biometric multi-factor authentication with blockchain technology to create a de- centralized electronic voting system. To guarantee vote in- tegrity and secure voter authentication, the system combines Ethereum smart contracts, wallet-based transaction signing, and facial recognition-based identity verication.

   The User Interface Layer, Authentication Layer, Application Layer, Smart Contract Layer, and Blockchain Layer are the ve main parts of the overall architecture, which is based on a layered decentralized model.

   1. System Architecture Overview

      Ganache is used to implement SecureVote as a decen- tralized application (DApp) that runs on a local Ethereum blockchain network. Face-API.js handles client-side biometric verication, and MetaMask serves as the wallet interface for transaction signing.

      The complete workow of the system consists of the following stages:

      1. Voter Registration
      2. Biometric Data Capture
      3. Identity Verication
      4. Vote Casting
      5. Blockchain Recording
      6. Result Computation

         Each stage ensures that voter authenticity and vote integrity are preserved throughout the election process.

   2. System Architecture Overview

      User

      Face-API

   3. Voter Registration Phase

      In addition to providing the necessary identity information, the voters face is photographed using a webcam interface during the registration process. Face-API.js is used to process the facial image and extract distinct facial embeddings using deep learning feature extraction methods.

      The system database safely stores only encoded facial feature vectors rather than raw facial images. MetaMask maps each voter to a distinct Ethereum wallet address.

      This mechanism ensures:

      • Unique voter registration
      • Secure encoding of biometric data
      • Mapping of veried identity to blockchain wallet address
   4. Multi-Factor Authentication Phase

      SecureVote implements a robust multi-factor authentication (MFA) mechanism consisting of:

      • Facial Biometric Verication
      • One-Time Password (OTP) Validation
      • Wallet-Based Signature Authentication

        The voters live facial image is taken during login and com- pared to the facial embedding that has been stored. Biometric verication is deemed successful if the similarity score is higher than a predetermined threshold.

        An OTP is created and sent to the voters registered contact method after facial authentication is successful. To continue, the voter must accurately enter the OTP.

        In order to verify that the vote is cryptographically signed by the rightful wallet owner, MetaMask lastly asks the voter to conrm the transaction.

        The dangers of identity theft, impersonation, and unau- thorized voting are greatly decreased by this multi-layered authentication system.

        OTP

        MetaMask

        Web3.js

        Smart Contract

        Ganache Blockchain

        Authentication Layer

        Application Layer

        Blockchain Layer

   5. Vote Casting and Smart Contract Enforcement

      The voter uses the user interface to choose a candidate after successful authentication. After that, the vote is sent to the Ethereum smart contract that has been deployed.

      The smart contract enforces two primary conditions:

      • The voter must be registered.
      • The voter must not have voted previously.

      The vote is recorded on the blockchain if both requirements are met. In order to avoid duplicate submissions, the smart contract increases the number of votes cast for the chosen candidate and marks the voters address as having cast a ballot.

      The vote is transparent, unchangeable, and impenetrable since it is recorded on the blockchain ledger.

   6. Blockchain Deployment Environment

      Ganache, a local Ethereum blockchain environment used for testing and development, is used to deploy the system. Ganache mimics actual blockchain operations, such as block conrmation, gas fee computation, and transaction mining.

      Fig. 1. SecureVote Layered Architecture

      To enable secure transaction signing, MetaMask estab- lishes a connection with the Ganache network. The deployed

      smart contract and the frontend application communicate via Web3.js.

      This deployment setup enables:

      • Real-time transaction validation
      • Gas consumption analysis

        Registration Facial Capture OTP

      • Secure decentralized execution
      • Controlled experimental testing

      Blockchain Conrmation

      Smart Contract

      MetaMask

   7. System Advantages

   <>The SecureVote framework offers the following advantages:

   • Decentralized vote storage using blockchain
   • Prevention of double voting through smart contract logic
   • Strong voter identity verication using facial recognition
   • Enhanced security through multi-factor authentication
   • Transparent and tamper-resistant vote recording SecureVote successfully resolves issues with identity au-

   thentication and vote integrity in contemporary electronic vot-

   ing systems by fusing biometric verication with blockchain immutability.

4. Methodology

   Through a structured, multi-phase process, SecureVotes methodology is intended to guarantee secure voter authen- tication, tamper-resistant vote recording, and the avoidance of duplicate voting. The four main operational stages of the suggested framework are Blockchain Verication, Voting, Authentication, and Registration.

   1. Registration Phase

      The registration phase ensures that each voter is uniquely identied and securely mapped to a blockchain address.

      • The voter submits required identication details through the web interface.
      • A live facial image is captured using the device camera.
      • Face-API.js processes the image and extracts a 128- dimensional facial embedding vector using deep learning- based feature extraction.
      • The embedding vector is stored securely in the database instead of the raw image to preserve privacy.
      • The voters Ethereum wallet address (via MetaMask) is linked to the registered prole.

        This phase ensures that each voter is uniquely associated with:

      • A biometric identity
      • A wallet address
      • A system registration record
   2. Biometric Verication Algorithm

      Facial recognition is carried out during login by contrasting the stored and live embeddings. Euclidean distance is used to calculate how similar two embeddings are:

      n

      Fig. 2. SecureVote Operational Flow

      • xi represents stored embedding values
      • yi represents live captured embedding values
      • n is the embedding dimension (128)

        If the distance D is below a predened threshold T , the user is authenticated.

        D < T (2)

        This ensures accurate identity verication while minimizing false acceptance and false rejection rates.

   3. Multi-Factor Authentication Phase

      To enhance security, SecureVote applies three authentication factors:

      • Something the user is Facial biometric identity
      • Something the user has Registered wallet (MetaMask)
      • Something the user receives One-Time Password (OTP)

        Before allowing vote submission, the OTP is generated server-side and veried.

        Only the following circumstances are considered successful authentication:

      • Facial match = Valid
      • OTP verication = Valid
      • Wallet signature = Conrmed

        This layered authentication model signicantly reduces im- personation risks.

   4. Voting Phase

      Once authentication is successful:

      1. The voter selects a candidate.
      2. A transaction request is generated.
      3. MetaMask prompts the voter to sign the transaction.
      4. The signed transaction is sent by Web3.js to the Ganache smart contract that has been deployed.
      5. The vote is recorded on the blockchain.
   5. Voting Phase
   6. Smart Contract Enforcement Logic

      The smart contract enforces vote validity using two key mappings:

      • validVoter[address]

        where:

        D =

        i=1

        (xi yi)2 (1)

        • hasVoted[address]

          The logical enforcement mechanism is represented as fol- lows:

          if validVoter[msg.sender] == true and hasVoted[msg.sender] == false:

          recordVote() hasVoted[msg.sender] = true

          else:

          rejectTransaction()

          This ensures:

      • Only registered voters can vote.
      • Voting is limited to one vote per voter.
   7. Blockchain Conrmation Phase

      Ganache simulates block mining and conrms the transac- tion. Once mined:

      • The vote count is updated.
      • The ledger entry becomes immutable.
      • A unique transaction hash is generated.
      • The vote is permanently recorded.

   The append-only and cryptographically secured nature of blockchain records makes it computationally impossible to change previously recorded votes.

5. Security Analysis

   An electronic voting systems ability to guarantee voter authenticity, vote integrity, condentiality, and resilience to malevolent attacks determines how secure it is. By com- bining multi-factor verication, biometric authentication, and blockchain technology, SecureVote satises these needs. The system is assessed against common security threats in this section.

   1. Threat Model

      SecureVote considers the following potential attack scenar- ios:

      • Identity Impersonation
      • Double Voting
      • Vote Tampering
      • Replay Attacks
      • Insider Manipulation
      • Unauthorized Access

        Each threat is analyzed below.

   2. Identity Impersonation

      Authentication methods that rely only on credentials, like passwords or identication numbers, are susceptible to im- personation attacks in conventional electronic voting systems. By combining OTP-based authentication with facial biometric verication, SecureVote reduces this risk.

      Because facial embeddings are unique to each voter and veried in real time, unauthorized users cannot cast votes even if login credentials are compromised. Further reducing unauthorized access is the additional OTP validation layer.

      Therefore, multi-factor authentication greatly reduces im- personation attacks.

   3. Double Voting Prevention

      When a voter tries to cast multiple ballots in the same election, this is known as double voting. This is stopped at the smart contract level by SecureVote.

      The smart contract maintains a mapping structure that records whether a wallet address has cast a vote:

      • validVoter[address]
      • hasVoted[address]

        The system prevents further submissions from the same wallet address by updating the hasVoted status to true after a vote is successfully cast.

        Duplicate voting attempts are essentially eliminated because this participation record cannot be changed due to the im- mutability of blockchain transactions.

   4. Vote Tampering Resistance

      Administrators or attackers may try to change votes that have been stored in centralized voting systems. By directly recording votes on the blockchain, SecureVoteremoves this vulnerability.

      A transaction is unchangeable once it has been veried on the Ethereum ledger (in the testing environment, this is done using Ganache). A recorded vote could not be changed without altering earlier blocks and reaching network-wide consensus due to distributed validation mechanisms and cryptographic hashing.

      Thus, vote tampering is effectively prevented.

   5. Replay Attack Protection

      Malicious replication of a previously legitimate transaction is known as a replay attack. Every submission is uniquely iden- tied thanks to Ethereums transaction model, which includes a unique nonce value for every transaction.

      Every vote submission is uniquely signed and veried because SecureVote uses wallet-based transaction signing via MetaMask. Without the correct nonce and private key signa- ture, transaction data cannot be reused, even if it is intercepted. Replay attacks are effectively prevented by this mechanism.

   6. Insider Threat Mitigation

      Administrators may purposefully alter or sway vote records in centralized systems, making them susceptible to insider ma- nipulation. After deployment, SecureVote removes centralized control.

      The logic of smart contracts is transparent and runs au- tomatically without human input. The contract rules ensure predictable and impenetrable execution since they cannot be changed without redeployment once they are deployed.

      Insider threats are greatly reduced by this decentralized execution model.

   7. Privacy Considerations

      SecureVote enhances voter privacy through multiple design decisions:

      • Raw facial images are not stored; only encoded embed- ding vectors are preserved.
      • Votes are linked to wallet addresses rather than publicly exposed personal identities.
      • Blockchain records store transaction data without reveal- ing sensitive biometric information.

        By separating blockchain records from biometric data stor-

        where:

        F 1 = 2 ×

        Precision × Recall Precision + Recall

        (5)

        age and avoiding direct identity exposure, the system lowers privacy risks.

   8. Security Summary

      A multi-layered security architecture is produced by com- bining blockchain immutability, smart contract enforcement, biometric verication, and multi-factor authentication.

      This multi-layered strategy guarantees:

      • Strong voter identity assurance
      • Prevention of duplicate voting
      • Tamper-resistant vote storage
      • Protection against replay and impersonation attacks
      • Reduced insider control

   All things considered, SecureVote offers a strong and safe foundation for decentralized electronic voting.

6. Experimental Evaluation

   Ganache was used as the local Ethereum blockchain net- work for the implementation and testing of the SecureVote system in a controlled development environment. Face-API.js was utilized for biometric authentication, and MetaMask was integrated with the frontend application for transaction signing. This section assesses the systems performance in terms of overall reliability, gas consumption, transaction efciency, and authentication accuracy.

   1. Experimental Setup

      The evaluation environment consisted of the following:

      • The local Ethereum blockchain was simulated using Ganache.
      • Smart contracts written in Solidity and deployed locally
      • Web3.js for blockchain interaction
      • MetaMask for wallet authentication
      • Face-API.js for client-side facial recognition
      • Node.js backend for OTP generation

        The experiments measured the blockchains performance and authentication success rate using multiple simulated voter registrations and voting attempts.

   2. Biometric Authentication Performance

      Standard classication metrics were used to assess the accuracy of facial verication.ssed.

      This is how precision is dened:

      • TP = True Positives
      • FP = False Positives
      • FN = False Negatives

        TABLE I

        Confusion Matrix for Facial Authentication

        	Predicted Positive	Predicted Negative
        Actual Positive	48	2
        Actual Negative	3	47

        Under typical lighting conditions, the facial authentication module demonstrated a high matching accuracy based on con- trolled testing. In order to reduce false acceptance and preserve system usability, the similarity threshold was adjusted.

        Time-bound validation and secure server-side generation en- sured that the OTP verication success rate stayed consistently high.

   3. Gas Consumption Analysis

      Every vote submission translates into the execution of a smart contract function. Ganache tracks the amount of gas used in each transaction.

      During several test runs, the average amount of gas used per vote transaction was noted. The ndings show that recording votes uses a moderate amount of gas, mostly due to smart contract state variable updates.

      By reducing unnecessary storage operations through smart contract design, gas cost overhead was reduced.

   4. Transaction Conrmation Time

      The time it took to conrm a transaction was calculated from the time MetaMask was signed until Ganache conrmed the block.

      The average conrmation time stayed low because Ganache mimics instant mining, allowing for almost real-time vote recording. Conrmation times in actual public blockchain networks can change based on changes in gas prices and network congestion.

   5. Double Voting Prevention Test

      Precision = TP

      TP + FP

      Recall is dened as follows:

      Recall = TP

      TP + FN

      The F1-score is dened as:

      (3)

      (4)

      To validate double voting prevention:

      1. A registered voter successfully cast a vote.
      2. The same wallet attempted to cast a second vote.
      3. The smart contract rejected the second transaction.

      This conrms that the hasVoted mapping logic effectively prevents duplicate voting attempts.

      TABLE II

      Performance Metrics of SecureVote

      Metric	Observed Value
      Facial Authentication Accuracy	96.2%
      OTP Verication Success Rate	99.1%
      Average Gas per Vote	42,315 Gas
      Average Transaction Time	4.5 sec
      Double Voting Prevention Rate	100%

   6. Performance Summary

      The experimental results show as follows:

      • Reliable biometric authentication performance
      • Effective multi-factor verication
      • Successful prevention of duplicate voting
      • Efcient smart contract execution
      • Minimal gas overhead in local blockchain deployment All things considered, SecureVote offers a performance and

   security balance that is appropriate for decentalized voting

   systems.

7. Comparative Analysis

   The majority of current solutions mainly concentrate on vote immutability and decentralized storage, despite the fact that many blockchain-based electronic voting systems have been proposed recently. Despite the fact that these contributions greatly increase transparency, they frequently lack multi-factor authentication integration and thorough identity verication procedures.

   In terms of important technical aspects like smart contract enforcement, decentralization level, authentication strength, and practical deployment, SecureVote is contrasted with pre- vious blockchain voting frameworks in this section.

   1. Comparison Criteria

      The following criteria were considered for comparison:

      • Blockchain-based vote storage
      • Biometric authentication integration
      • Multi-factor authentication (MFA)
      • Double voting prevention logic
      • Real decentralized application (DApp) deployment
      • Assessment of practical implementation
   2. Comparative Evaluation

      Distributed ledger security and tamper-resistant vote record- ing are key features of the majority of systems surveyed. Nevertheless, a lot of frameworks only use digital credentials or wallet-based verication for authentication. Cryptographic keys do not ensure that the person casting the ballot is the right voter, even though they offer secure identity mapping at the transaction level.

      In contrast, SecureVote integrates:

      • Facial biometric verication
      • Secondary authentication using OTP
      • Wallet-based digital signature

        When compared to credential-only systems, this layered authentication method greatly improves identity assurance.

        Additionally, a number of earlier works lack complete DApp implementation and are still conceptual or simulation- based. Using Ganache and MetaMask integration, SecureVote offers a comprehensive end-to-end working framework that is implemented on a blockchain environment.

   3. Comparative Table

      TABLE III Comparison of Voting Systems

      Feature	Traditional	Blockchain	SecureVote
      Centralized Control	Yes	No	No
      Vote Immutability	Limited	Yes	Yes
      Biometric Auth.	No	No	Yes
      MFA Support	No	Limited	Yes
      Smart Contracts	No	Yes	Yes
      Double Voting	Weak	Moderate	Strong
      Deployment	Yes	Conceptual	Yes

   4. Discussion

   The comparison makes it abundantly evident that although blockchain-only voting systems increase transparency and thwart tampering, they frequently ignore robust voter identity assurance measures. If private keys are compromised, systems that only use wallet credentials could still be vulnerable.

   By incorporating multi-factor verication and biometric au- thentication into the blockchain voting procedure, SecureVote overcomes this restriction. Voter authenticity and vote integrity are both protected by this combination.

   Compared to other methods, SecureVote offers a more complete and secure electronic voting framework by combin- ing biometric identity verication with decentralized ledger security.

8. Conclusion and Future Work

1. Conclusion

   In order to improve electoral security and transparency, this paper introduced SecureVote, a blockchain-based electronic voting framework that combines multi-factor verication with facial biometric authentication. To provide robust voter iden- tity assurance, the system integrates client-side facial recog- nition and OTP-based authentication with Ethereum smart contracts for tamper-resistant vote storage.

   SecureVote uses decentralized ledger technology to remove single points of failure and stop unauthorized vote modica- tion, in contrast to conventional electronic voting systems that depend on centralized control. While smart contract enforce- ment successfully avoids double voting, the incorporation of biometric verication enhances defenses against impersonation attacks.

   A Ganache-based Ethereum environment was used for the experimental evaluation, which showed minimal gas overhead, effective transaction processing, and dependable authentication performance. The system ensured secure blockchain-based

   vote recording while maintaining high biometric verication accuracy.

   SecureVote tackles the issues of voter authenticity and vote integrity in electronic voting systems by fusing biomet- ric multi-factor authentication with decentralized blockchain infrastructure. The suggested framework offers a scalable, transparent, and safe basis for decentralized and remote voting applications.

2. Future Work

   While SecureVote exhibits robust security and performance attributes in a controlled testing setting, a number of improve- ments could be investigated in subsequent studies:

   • deployment on a public Ethereum test network to assess variations in gas prices, latency, and scalability in the real world.
   • incorporating cutting-edge liveness detection methods to fortify facial authentication against presentation and spoong attacks.
   • use of cryptographic methods that protect privacy, like zero-knowledge proofs, to increase voter anonymity while preserving veriability.
   • Optimization of smart contract architecture to reduce gas consumption in large-scale elections.
   • Performance analysis to determine system robustness and scalability in high voter concurrency scenarios.
   • Exploration of decentralized identity (DID) frameworks for improved voter identity management.

Future developments in scalable blockchain infrastructures, decentralized identity systems, and cryptographic privacy tech- niques can all improve the sturdiness, usefulness, and potential for adoption of blockchain-enabled electronic voting systems.

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