Author: Denis Avetisyan
Researchers propose a new e-voting system designed to withstand future quantum computing threats and enhance election integrity through biometric authentication and immutable blockchain records.
This review details a framework integrating post-quantum cryptography (Falcon), biometric identity validation (MobileFaceNet, AdaFace), and blockchain technology for secure and transparent e-voting.
Despite growing concerns about the security of digital voting systems, particularly in light of advancing quantum computing capabilities, ensuring both voter authentication and data integrity remains a significant challenge. This paper introduces ‘A Quantum-Secure and Blockchain-Integrated E-Voting Framework with Identity Validation’-a novel architecture combining post-quantum cryptography, specifically the Falcon signature scheme, with biometric authentication and a permissioned blockchain. The proposed system achieves robust identity validation via facial recognition, tamper-proof vote storage, and demonstrable efficiency with minimal blockchain overhead. Could this integrated approach represent a viable pathway toward truly secure, transparent, and scalable electronic voting for the future?
The Illusion of Security: Facing the Evolving Threats
Contemporary voting systems, encompassing everything from paper ballots to electronic machines, are increasingly vulnerable to a diverse range of manipulation attempts. These threats aren’t limited to the digital realm; physical security breaches, such as ballot box tampering or intimidation at polling places, remain significant concerns. The escalating sophistication of these attacks-ranging from targeted disinformation campaigns designed to suppress turnout to attempts to directly alter vote tallies-demands a fundamental reassessment of election security protocols. Consequently, a robust, multi-faceted approach is critical, encompassing enhanced cybersecurity for electronic systems, rigorous chain-of-custody procedures for paper ballots, comprehensive voter roll maintenance, and increased physical security at polling locations. Without these measures, the integrity of democratic processes and public confidence in election outcomes are increasingly at risk.
Election security is facing an escalating arms race, demanding defenses beyond traditional cybersecurity. Increasingly, attackers are employing presentation attacks – sophisticated methods of deceiving biometric authentication systems, such as high-fidelity spoofing of fingerprints or facial features, potentially compromising voter verification. Simultaneously, the development of quantum computing poses a long-term, but critical, threat; current encryption standards, which safeguard voter data and election results, are vulnerable to decryption by sufficiently powerful quantum computers. Consequently, a reactive approach is insufficient. Experts advocate for a proactive, multi-layered defense incorporating advanced biometric countermeasures, post-quantum cryptography – encryption algorithms resistant to quantum attacks – and continuous monitoring to detect and mitigate emerging threats. This holistic strategy aims not only to secure current elections but also to future-proof the voting process against the evolving landscape of technological sophistication and malicious intent.
Sustaining public confidence in electoral outcomes demands more than simply keeping ballots secret; it necessitates a demonstrable openness and the ability for citizens to independently confirm the accuracy of results. A truly secure election isn’t one hidden behind layers of encryption, but one where processes are auditable and understandable. This requires implementing systems that allow for risk-limiting audits, where statistical methods are used to verify the outcome without recounting every vote, and embracing technologies like blockchain for immutable record-keeping. Moreover, clear communication about security protocols and making audit results publicly available are crucial steps in fostering trust. Without this combination of confidentiality, transparency, and verifiability, even technically sound systems risk being undermined by perceptions of manipulation, eroding the very foundation of democratic governance.
Verifying the Voter: A Façade of Accuracy
Biometric authentication is implemented to verify voter identity at the point of voting, restricting participation to registered and eligible citizens. This process utilizes unique biological traits – specifically facial features – to confirm identity against a secure database of registered voters. The system captures a live facial image of the voter, which is then compared to the stored image associated with their registration. Successful matching confirms the voter’s eligibility, preventing fraudulent voting and ensuring the integrity of the election process. This approach reduces the risk of impersonation and other forms of voter fraud compared to traditional identification methods.
Voter identity verification relies on AdaFace, a face recognition framework that generates Face Embeddings using the ResNet-50 deep neural network architecture. These embeddings are numerical representations of facial features, allowing for accurate comparison against a database of eligible voters. AdaFace has demonstrated a 98% accuracy rate on the IJB (IJB-C) benchmark, a large-scale dataset used for evaluating face recognition performance under diverse conditions, including variations in pose, illumination, and expression. This high level of accuracy contributes to the system’s ability to reliably authenticate voters and prevent fraudulent activity.
AdaFace incorporates Quality-Adaptive Margin (QAM) techniques to improve the reliability of face recognition under varying input conditions. QAM functions by dynamically adjusting the margin parameter – the distance required between a face embedding and a negative example to be considered dissimilar – based on the assessed quality of the input image. Lower quality images, typically exhibiting blur, noise, or poor lighting, receive a reduced margin, allowing for greater tolerance in embedding comparisons. Conversely, higher quality images utilize a larger margin, demanding greater dissimilarity for negative matches. This adaptive approach mitigates the impact of image degradation on recognition accuracy, preventing false negatives caused by minor variations in lower quality images while maintaining strict discrimination for clear, high-resolution inputs.
The Illusion of Liveness: Detecting the Undetectable
An integrated Anti-Spoofing Mechanism addresses the threat of presentation attacks by leveraging the MobileNetV3 convolutional neural network as its core architecture. This foundation provides a computationally efficient base for detecting spoofing attempts, allowing for real-time processing and analysis of facial data. The mechanism operates by analyzing visual features extracted from the input, differentiating between genuine facial features and those presented through various spoofing methods, such as printed images, video replays, or 3D masks. The use of MobileNetV3 prioritizes performance without sacrificing accuracy in the detection of fraudulent access attempts.
MobileFaceNet is implemented as an extension of the MobileNetV3 architecture to deliver both real-time facial verification and anti-spoofing functionality. This system achieves a 97% accuracy rate in detecting spoofing attempts when evaluated on the NUAA and Replay-Attack datasets. Performance is characterized by its ability to discriminate between live and presented facial images, contributing to increased system security by preventing unauthorized access via fraudulent means.
The system’s ability to differentiate between live and spoofed facial presentations is quantified by Area Under the Curve (AUC) metrics achieved on standard datasets. Performance on the NUAA dataset yielded an AUC of 0.996, indicating a 99.6% accuracy in distinguishing legitimate faces from spoof attempts. Similarly, testing against the Replay-Attack dataset resulted in an AUC of 0.9896, demonstrating 98.96% accuracy under replay attack conditions. These AUC values represent the aggregate performance across all possible classification thresholds and serve as a robust indicator of the system’s anti-spoofing efficacy.
The system employs a multi-layered security architecture to minimize the potential for fraudulent access. This approach integrates biometric authentication – establishing user identity through unique biological traits – with dedicated anti-spoofing measures designed to detect and reject artificially created or manipulated biometric presentations. By requiring successful completion of both authentication and liveness detection, the system substantially decreases the probability of unauthorized access attempts, as attackers must overcome multiple security barriers. This layered defense is crucial for protecting sensitive data and resources from malicious actors attempting to bypass security protocols through the use of spoofing techniques.
Decentralized Distrust: The Promise and Peril of Blockchain Voting
A foundational element of this secure election system is a decentralized ledger built upon Ethereum blockchain technology. This infrastructure moves voter information and recorded votes beyond the control of any single entity, distributing it across a network of computers. Consequently, any attempt to fraudulently alter records requires controlling a majority of this network – a computationally prohibitive task. The blockchain’s inherent cryptographic security and immutability ensure that once a vote is recorded, it cannot be tampered with or deleted, establishing a permanent, auditable trail. This design dramatically reduces the risk of voter fraud and manipulation, fostering increased trust in election outcomes by providing a transparent and verifiable record accessible to authorized parties.
The implementation of smart contracts represents a pivotal advancement in election administration by automating traditionally manual processes. These self-executing agreements, coded onto the blockchain, handle critical functions like voter registration verification and the tabulation of votes with minimal human intervention. This automation significantly reduces the potential for errors inherent in manual counting and verification, while simultaneously increasing the speed and efficiency of the electoral process. By predefining the rules and outcomes within the contract’s code, the system ensures consistent and transparent execution, minimizing opportunities for manipulation or fraud. The result is a more reliable and auditable election system, fostering greater public trust in the integrity of the results.
The integrity of digital voting systems relies heavily on robust cryptographic protections, and this system integrates Falcon Post-Quantum Cryptography to address vulnerabilities posed by both current and future computing technologies. Utilizing parameter sets like Falcon-512 and Falcon-1024, the system secures voter data against attacks from classical computers while simultaneously preparing for the advent of quantum computing. Performance benchmarks demonstrate efficient operation, with encryption taking between 8.64 and 27.45 milliseconds, depending on the selected security level – ranging from NIST 1 to 5 – and decryption completed in 2.8 to 10.5 milliseconds even under moderate load, handling 20 to 80 concurrent requests. This combination of strong cryptographic algorithms and optimized performance ensures a secure and scalable solution for protecting the confidentiality and authenticity of votes.
A key innovation within the proposed voting system lies in its remarkably efficient use of blockchain resources, specifically minimizing ‘gas’ consumption – the computational cost of transactions. Analyses reveal that registering a voter consumes only 3.4% of the total gas expenditure, while the act of casting and recording a vote requires a mere 0.2%. This low overhead is crucial for scalability and accessibility, allowing the system to handle a large volume of voters and transactions without incurring prohibitive costs. By dramatically reducing gas consumption compared to many existing blockchain applications, the system optimizes blockchain efficiency and makes secure, transparent elections more economically viable for widespread implementation.
The pursuit of an unassailable e-voting system, as detailed in this framework integrating post-quantum cryptography and biometric authentication, feels less like engineering and more like constructing a particularly elaborate sandcastle against the tide. The authors champion Falcon and MobileFaceNet as bulwarks against present and future attacks, a noble effort. Yet, one recalls Edsger W. Dijkstra’s observation: “It’s not enough to do your best; you must do your best at the right time.” Deploying even the most mathematically sound cryptography doesn’t guarantee immunity; production environments will inevitably uncover unforeseen vulnerabilities. Tests are, after all, a form of faith, not certainty. The elegance of a blockchain-integrated system matters little when a simple denial-of-service attack renders it inaccessible on election day. The framework’s layered security is commendable, but history suggests that tomorrow’s attack surface will reside not in the algorithms themselves, but in the human systems surrounding them.
What Breaks First?
The framework, as presented, solves a great many theoretical problems. It layers defenses – post-quantum cryptography, biometrics, and blockchain – in a manner that satisfies the current risk models. The inevitable question, however, isn’t if a vulnerability will be discovered, but where. Anything self-healing just hasn’t broken yet. The biometric component, while employing anti-spoofing measures, presumes a static definition of “liveness.” Production will quickly demonstrate the ingenuity of determined adversaries. Documentation, predictably, will lag behind.
Future work will undoubtedly focus on scaling. A system secure enough for a small testbed is not necessarily secure – or even functional – under real-world load. The integration of Falcon, while promising, demands constant monitoring of its performance characteristics as quantum computing advances. The true cost isn’t in the initial deployment, but in the continuous adaptation required to stay ahead of newly discovered attack vectors. If a bug is reproducible, it suggests a stable system-a rare and fleeting achievement.
Ultimately, this is a sophisticated exercise in threat modeling. The framework identifies current vulnerabilities and attempts to mitigate them. But the landscape of threats is not static. The next iteration will likely address the flaws exposed by real-world deployment, creating a cycle of defense and counter-defense. And so it goes.
Original article: https://arxiv.org/pdf/2511.16034.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
See also:
- Rebecca Heineman, Co-Founder of Interplay, Has Passed Away
- 9 Best In-Game Radio Stations And Music Players
- Gold Rate Forecast
- Byler Confirmed? Mike and Will’s Relationship in Stranger Things Season 5
- All Exploration Challenges & Rewards in Battlefield 6 Redsec
- Ships, Troops, and Combat Guide In Anno 117 Pax Romana
- J Kozma Ventures Container In ARC Raiders (Cold Storage Quest)
- Upload Labs: Beginner Tips & Tricks
- Drift 36 Codes (November 2025)
- 8 Games That Predicted the Future of the Real World
2025-11-21 17:49