E-Voting Using Blockchain

Abstract

Democratic rule is based on safe and open electoral systems that can preserve their authenticity. The conventional voting processes are hampered significantly by electoral fraud and security breaches as well as centralized management and inefficiencies in auditability and accessibility. The issues of electoral systems reduce public trust along with lowering the credibility of election results. This paper introduces a new three-tier blockchain-based e-voting system designed to enhance voter privacy alongside system scalability and end-to-end verifiability to restore trust in electoral processes. Voter verification at Layer 1 (Identity Verification) combines Decentralized Identity (DID) with Zero-Knowledge Proofs (ZKP) and multimodal biometric techniques involving fingerprint scanning, facial recognition technology, and voice analysis. The system enables only the participation of valid voters while also protecting their private data and fulfilling different user needs. The system\'s Layer 2 (Vote Casting & Secure Storage) employs a hybrid consensus algorithm combining Byzantine Fault Tolerance (BFT) and Delegated Proof-of-Stake (dPoS) to store votes securely while reducing energy consumption. The system leverages Triple-Blind Signatures to provide complete voter anonymity by decoupling voter identities from their votes as well as any accompanying metadata. Lattice-based post-quantum cryptography is employed to encrypt votes which are distributed across sharded blockchain subnets for enhanced performance without sacrificing fault tolerance. The system accumulates votes via Merkle roots and verifies them via zk-SNARKs in Layer 3 (Result Processing & Transparency) that allows public observation without compromising voter privacy. A Live Audit Dashboard provides voters with the capability to check their vote in real time which facilitates transparent and accountable voting processes.

Introduction

The integrity, transparency, and inclusivity of elections are critical for democracy. With growing digital transformation, electronic voting (e-voting) promises more efficient and accessible elections but faces challenges like security risks, lack of auditability, centralized control, and privacy concerns. Traditional centralized e-voting systems are vulnerable to manipulation and exclusion, especially for people with disabilities.

Blockchain technology offers a decentralized, tamper-resistant foundation for e-voting by providing transparent, immutable, and secure record-keeping. However, challenges remain, such as scalability, voter anonymity, quantum resistance, and usability.

The paper proposes a novel three-layer blockchain-based e-voting system addressing these issues:

• Layer 1: Identity Verification
  Uses decentralized identities (DID), zero-knowledge proofs (ZKP), and multimodal biometrics (fingerprint, face, voice) to ensure inclusive, privacy-preserving voter authentication without central data storage.

• Layer 2: Vote Casting and Secure Storage
  Implements triple-blind signatures to unlink voter identity from votes, employs quantum-resistant lattice cryptography, and achieves scalability via a hybrid Byzantine Fault Tolerance and Delegated Proof-of-Stake consensus on sharded blockchain subnets.

• Layer 3: Result Processing and Transparency
  Aggregates votes into a national ledger using Merkle root hashes and zk-SNARKs to maintain privacy while enabling public verification through a live audit dashboard.

This architecture uniquely combines advanced cryptography, blockchain decentralization, and inclusivity features, aiming for a secure, scalable, transparent, and legally compliant e-voting system.

The text also reviews existing blockchain-based voting projects and academic research, noting their limitations in scalability, privacy, inclusivity, post-quantum security, and legal compliance, and highlights how the proposed system overcomes these gaps.

Conclusion

This study suggests a holistic three-layer blockchain-enabled architecture for electronic voting systems that can solve the long-standing problems of security, transparency, privacy, and voter inclusivity in digital elections. The system simulates the entire voting process in three phases that are independent but interrelated: decentralized identity verification, secure and anonymous vote casting, and transparent result aggregation. Each layer is underpinned by cryptographic protocols specially designed to suit the specific needs of its phase, with end-to-end verifiability and compliance with ever-changing legal and ethical standards being guaranteed. With decentralized identity models, zero-knowledge proofs, and multimodal biometric verification, the proposed solution presents a secure alternative to conventional voter registration and eligibility check. Application of lattice-based post-quantum cryptography and triple-blind signatures upon vote casting protects future-proof confidentiality of ballots as well as encryption, while hybrid consensus algorithms provide fast vote verification at high throughputs without loss of decentralization. Lastly, transparent result aggregation through Merkle root calculation and zk-SNARK-based proof validation offers an openly auditable outcome, including the ability for real-time verification using an onboard audit dashboard. As opposed to traditional e-voting systems that are plagued by either centralization or poor scalability, this design is scalable, modular, and responsive to different infrastructural readiness levels. It not only meets international standards for data protection but also envisions challenges in the future, such as the quantum computing challenge, cross-jurisdictional elections, and voter accessibility. While the system is still in conceptual and simulation phases, it presents a workable model for future deployment. Further research will involve prototyping the framework, testing its performance under actual-world constraints, and investigating its use in national elections, institutional governance, and other secure digital decision-making contexts.

References

[1] N. Kshetri and J. Voas, “Blockchain-enabled e-voting,” *IEEE Software*, vol. 35, no. 4, pp. 95–99, Jul.–Aug. 2018. [2] M. McCorry, S. F. Shahandashti, and F. Hao, “A smart contract for boardroom voting with maximum voter privacy,” in *Proc. Int. Conf. Financial Cryptography and Data Security*, 2017, pp. 357–375. [3] A. Zyskind, O. Nathan, and A. Pentland, “Decentralizing privacy: Using blockchain to protect personal data,” in *Proc. IEEE Security and Privacy Workshops (SPW)*, 2015, pp. 180–184. [4] V. Buterin, “A next-generation smart contract and decentralized application platform,” Ethereum White Paper, 2014. [Online]. Available: https://ethereum.org/en/whitepaper/ [5] S. Nofer, P. Gomber, O. Hinz, and D. Schiereck, “Blockchain,” *Business & Information Systems Engineering*, vol. 59, no. 3, pp. 183–187, 2017. [6] Sovrin Foundation, “Sovrin: A Protocol and Token for Self-Sovereign Identity and Decentralized Trust,” 2018. [Online]. Available: https://sovrin.org/wp-content/uploads/2018/03/Sovrin-Protocol-and-Token-White-Paper.pdf [7] J. Benet, “IPFS: Content Addressed, Versioned, P2P File System,” 2014. [Online]. Available: https://ipfs.io/ [8] L. Goodell, “The future of cryptography: Preparing for quantum computing,” *IEEE Potentials*, vol. 37, no. 1, pp. 11–17, Jan.–Feb. 2018. [9] J. Grover, M. S. Asghar, and M. Z. A. Bhutta, “Secure e-voting using blockchain: A systematic review,” *Computers & Security*, vol. 117, Mar. 2022. [10] M. Conti, C. Lal, and S. Ruj, “A survey on security and privacy issues of blockchain technology,” *IEEE Communications Surveys & Tutorials*, vol. 21, no. 2, pp. 1169–1193, 2019. [11] D. Chaum, R. Carback, and B. Adida, “Scantegrity: End-to-end voter-verifiable optical-scan voting,” *IEEE Security & Privacy*, vol. 6, no. 3, pp. 40–46, May–Jun. 2008. [12] L. Luu et al., “A secure sharding protocol for open blockchains,” in *Proc. ACM SIGSAC Conf. Computer and Communications Security (CCS)*, 2016, pp. 17–30. [13] M. Al-Bassam, “Scalable transparent zk-SNARKs for blockchain applications,” *arXiv preprint arXiv:1807.07660*, 2018. [14] C. Costello et al., “CRYSTALS-Kyber and CRYSTALS-Dilithium: Post-quantum cryptographic schemes,” *National Institute of Standards and Technology (NIST)*, 2022. [Online]. Available: https://csrc.nist.gov/Projects/post-quantum-cryptography [15] Google AI, “TensorFlow: An end-to-end open source machine learning platform,” 2023. [Online]. Available: https://www.tensorflow.org/ [16] G. Bradski, “The OpenCV library,” *Dr. Dobb’s Journal of Software Tools*, 2000. [Online]. Available: https://opencv.org/ [17] Hyperledger Foundation, “Hyperledger Fabric Documentation,” 2023. [Online]. Available: https://hyperledger-fabric.readthedocs.io/ [18] J. Eberhardt and S. Tai, “ZoKrates: Scalable privacy-preserving off-chain computations,” in *Proc. IEEE Int. Conf. Internet of Things (iThings)*, 2018, pp. 1084–1091. [19] M. Finck, “Blockchain and the General Data Protection Regulation: Can distributed ledgers be squared with European data protection law?” *European Parliamentary Research Service*, 2019. [Online]. Available: https://www.europarl.europa.eu/RegData/etudes/ STUD/2019/634445/EPRS_ STU(2019)634445_EN.pdf [20] S. Park, D. Reischuk, and M. Kim, “Usability challenges in blockchain-based voting systems,” in *Proc. ACM Human-Computer Interaction Conf.*, 2021, pp. 1–21.

Copyright

Copyright © 2025 Dinesh Singh Dhakar, Mrinal Kohli, Saurav Sati, Varun Saini, Diwansh Thakur, Khushi Barthwal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.