Remote Procedure Call (RPC) services have become a primary gateway for users to access public blockchains. While they offer significant convenience, RPC services also introduce critical privacy challenges that remain insufficiently examined. Existing deanonymization attacks either do not apply to blockchain RPC users or incur costs like transaction fees assuming an active network eavesdropper. In this paper, we propose a novel deanonymization attack that can link an IP address of a RPC user to this user's blockchain pseudonym. Our analysis reveals a temporal correlation between the timestamps of transaction confirmations recorded on the public ledger and those of TCP packets sent by the victim when querying transaction status. We assume a strong passive adversary with access to network infrastructure, capable of monitoring traffic at network border routers or Internet exchange points. By monitoring network traffic and analyzing public ledgers, the attacker can link the IP address of the TCP packet to the pseudonym of the transaction initiator by exploiting the temporal correlation. This deanonymization attack incurs zero transaction fee. We mathematically model and analyze the attack method, perform large-scale measurements of blockchain ledgers, and conduct real-world attacks to validate the attack. Our attack achieves a high success rate of over 95% against normal RPC users on various blockchain networks, including Ethereum, Bitcoin and Solana.
Prof. C. V. Nalawade, V. S. Bhosale, S. A. Khetre, K. B. Kadam
In recent years, the convergence of Artificial Intelligence (AI), Blockchain, and Biometric technologies has revolutionized digital identity verification and secure electronic voting systems. The demand for transparent, tamper-proof, and fraud-resistant voting mechanisms has prompted researchers to explore distributed ledgers combined with intelligent authentication systems. This paper presents a comprehensive survey of AI-enabled blockchain voting architectures integrating biometric verification. The study explores technical advancements from 2020 to 2025 in decentralized voting, intelligent fraud detection, and privacy-preserving biometric mechanisms. We classify recent research trends, analyze current methodologies, and identify open challenges and future directions in implementing scalable and trustworthy e-voting frameworks. The analysis concludes that integrating blockchain’s immutability with AI-driven fraud detection and biometric identification ensures high security, transparency, and reliability in modern digital democracies.
The security of blockchain systems relies on the honest majority assumption. However, strategic mining threatens this assumption, because selfish miners can gain more block rewards than honest miners by attacks such as withholding blocks. Due to its significant implication, blockchain mining games have been studied in PoW and PoS under various settings using different methods. Nonetheless, this paper argues that the practical limitation of random beacons has not been exploited in strategic mining in PoS blockchains. Current PoS blockchains use random beacons to randomly select validators for each slot. However, the randomness is usually fixed for multiple slots, due to the latency of distributed random beacon protocols. This indicates that validators actually know some information about the election result in the future, which contrasts with the Markov process models in previous analysis. Using this information, this paper presents a close to optimal mining strategy based on an optimal interval scheduling algorithm for each epoch. For proof-of-stake protocols with no propagation delay, we show that a validator with arbitrary proportion of stake can strictly benefit from strategic mining and get significantly higher block rewards than the previous strategies.
Nurhajar Anugraha, Muhammad Riswanto, Lindawati Lindawati, Asrul Asrul
Penelitian ini bertujuan untuk mengembangkan sistem e-voting berbasis blockchain dengan autentikasi biometrik sidik jari serta penerapan protokol zero-knowledge proofs sebagai pengamanan tambahan terhadap data pemilih dan hasil suara. Permasalahan utama yang dihadapi dalam sistem pemungutan suara elektronik konvensional adalah rendahnya kepercayaan terhadap keamanan data dan potensi manipulasi hasil. Metode penelitian yang digunakan mencakup perancangan sistem dengan arsitektur client-server, implementasi teknologi blockchain untuk pencatatan suara yang terenkripsi, serta integrasi biometrik sidik jari menggunakan BiometricPrompt API pada Android. Selain itu, sistem diverifikasi dengan kode OTP melalui email institusional sebagai bentuk validasi ganda pengguna. Hasil pengujian menunjukkan bahwa sistem dapat berjalan dengan baik dan memberikan keamanan yang tinggi karena setiap data suara tersimpan secara permanen dan tidak dapat diubah di jaringan blockchain. Autentikasi biometrik juga memastikan bahwa setiap pemilih terverifikasi secara unik sehingga tidak terjadi pemungutan suara ganda. Dengan demikian, sistem e-voting ini dinilai layak diterapkan untuk lingkungan akademik dan dapat dikembangkan lebih lanjut untuk pemilihan umum berskala lebih besar.
Jimmy Jimmy, Kenny Rimba, Vincent Vincent, Ronsen Purba · 5 authors
E-voting systems are prone to challenges such as lack of transparency, risks of data manipulation, and dependence on centralized authorities, which can undermine trust in electoral processes. This research develops a blockchain-based e-voting system on the Polygon network, leveraging smart contracts and Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (ZK-SNARK) to enhance security, transparency, and voter anonymity. The study employs an application development approach, implementing a structured methodology with initialization, registration, voting, and tallying phases. Smart contracts automate voter verification, vote casting, and result tabulation, while ZK-SNARK ensures voters can cast ballots anonymously without revealing their identities. The system’s transparency and immutability are tested using PolygonScan, demonstrating effective prevention of manipulations like double voting through cryptographic credentials (nullifier, commitment, and nullifier hash) and Merkle Tree structures. Results indicate that the system provides a secure, verifiable, and decentralized framework for elections. This implementation offers a robust foundation for future e-voting systems, promoting trust and integrity in digital voting processes.
Persistent electoral irregularities—ranging from vote manipulation and ballot stuffing to logistical failures and post-election violence—continue to undermine democratic consolidation across developing democracies. Nigeria, Africa’s largest democracy, epitomizes this crisis, where recurrent allegations of fraud, digital failures, and institutional mistrust have eroded public confidence in electoral outcomes. This paper proposes a secure, transparent, and technically robust blockchain-based electoral framework tailored for developing democracies. Leveraging the core attributes of blockchain—immutability, decentralization, real-time auditability, and cryptographic security—we design a technical architecture for voter registration, ballot casting, vote tallying, and public verification. The system integrates smart contracts, Proof of Authority (PoA) consensus, cryptographic identity verification, and zero-knowledge proofs to ensure integrity, privacy, and resilience. We analyze implementation challenges including the digital divide, cybersecurity threats, and legal gaps, and propose a phased, stakeholder-driven roadmap anchored in Nigeria’s institutional context. Comparative insights from Estonia, Sierra Leone, and Brazil underscore the importance of local ownership, institutional autonomy, and civic literacy. The paper contributes a practical, context-sensitive blueprint for blockchain-based electoral reform, bridging the gap between theoretical innovation and real-world deployment in fragile democratic ecosystems.
Prof. Madhavi Bhosale, Abhishek Kangude, Vedant Khandare, Sunil Kajave
Abstract In recent years, advancements in blockchain technology have paved the way for creating transparent, secure, and decentralized digital ecosystems. This paper presents a blockchain-based electronic voting (e-voting) system designed to overcome the limitations of traditional and centralized electronic voting methods. The proposed system integrates Solidity-based smart contracts, a Python middleware API using Web3.py, and a Flutter frontend to create a secure, verifiable, and user-friendly voting platform. The architecture ensures voter anonymity, immutability of votes, and real-time result verification through blockchain’s decentralized ledger. The system employs MetaMask for voter authentication, enabling a one-person-one-vote mechanism and eliminating centralized control or tampering risks. Experimental simulations using Ganache demonstrate efficient transaction processing, transparent result computation, and tamper-proof data storage. The proposed solution enhances security, transparency, and trust in digital elections and serves as a foundation for scalable, real-world implementations in organizational, academic, and governmental voting scenarios. This research contributes toward developing next-generation decentralized voting infrastructures that reinforce democratic integrity and public confidence in electoral processes. · Keywords : Blockchain Technology; E-Voting System; Smart Contracts; Decentralized Applications (DApps); Solidity; Ethereum; Python Web3.py; Flutter Frontend; MetaMask Authentication; Digital Elections; Voter Privacy; Transparency; Immutability; Secure Voting; Electronic Governance
Shwetha K R, Divya G S, Bhavan Pande, Darshan K · 6 authors
Due to the ever-increasing demand to use safe and reliable electronic votes, a blockchain-based secure voting system has been developed to enhance transparency, trustfulness, and voter recognition. This system eliminates such issues as voting fraud, impersonation, and manipulating the results by means of biometric verification and decentralized blockchain ledger. The voters are matched to a facial-recognition database containing previously registered voters before voting. It is authenticated by a K-Nearest Neighbors (KNN) approach as it works well on classifying facial features and is not very laborious. After the vote is successfully authenticated, it is stored and signed on a blockchain network where it cannot be altered by another party. The features of smart contracts ensure the safety of voting, the correct counting of votes, and the awareness of each network node of what is happening. The cryptography of hashing and decentralized make certain that the votes are immutable, due to the decentralized structure of blockchain and consensus mechanisms. The face-matching module ensures that only the qualified individuals are allowed to vote. The system also supports mass elections and guarantees the ease of interaction among the voters. It was designed in such a way that it is scalable and user friendly. Trust, security, and efficiency are enhanced in the system through biometrical authentication, distributed ledger technology, encryption, and classification through machine-learning. It is highly dependable in how to conduct the current digital elections.
A blockchain-enabled Model integrates blockchain technology with Intrusion Detection Systems to enhance the security of Internet of Things (IoT) networks. It ensures data integrity, decentralization, and tamper-proof logging of intrusion detection. The approach improves trust, transparency, and real-time threat detection in distributed IoT environments. The existing blockchain-based IDS approaches, Blockchain Enabled (BCE-IoT), uniquely integrate blockchain consensus with federated-style local training, lightweight cryptography, and Shapley Additive Explanations (SHAP)-based explainability, ensuring both security and interpretability in IoT environments. The proposed work combines Blockchain technology with explainable artificial intelligence solutions to create a new cybersecurity Model that strengthens intrusion detection within IoT networks. The proposed model enhances transparency in tracking cyberattacks by combining blockchain security storage capabilities with SHAP, an explainable AI. This research utilises machine learning and artificial intelligence to detect threats in real-time, countering Distributed Denial of Service (DDoS), Denial of Service (DoS), scanning, Cross-Site Scripting (XSS), injection, password, and backdoor attacks. BCE-IoT delivers more precise security by combining blockchain’s permanent data features and AI anomaly detectors, thereby reducing security alert mistakes. The performance effectiveness of Blockchain-Enabled IoT surpasses that of the Content Integrity Detection System. It combines Blockchain and Software-Defined Networking to enhance security in network environments, utilising blockchain-based mutual confirmation for software-defined networking to detect and block cyber threats. The evaluation establishes BCE-IoT as an effective IoT network security solution that delivers strong cybersecurity features, is adaptable to modern connected environments, and offers interpretable security solutions. The performance evaluations demonstrate that BCE-IoT provides a robust, flexible, and interpretable cybersecurity solution suitable for modern IoT environments.
The integrity, coupled with the transparency of electoral systems, is vital for the existence of a democratic society if that society is to function well. Often, conventional electronic voting mechanisms are criticized for their security vulnerabilities, with a lack of transparency, together with limited public trust. Blockchain technology has come about to be a possible enabler for trustless and immutable systems. However, such a standard, privacy-preserving, verifiable voting model remains elusive. This work seeks to fill this void with the use of a blockchain e-voting system that uses QR codes to validate voters, cryptographically ensures integrity with the EFFT-SWIFFT hash, and also handles ballots through smart contracts. A feature matrix together with a visual chart was used in a systematic literature review of 28 peer-reviewed papers to analyze and compare authentication methods, transparency techniques, consensus mechanisms, and scalability solutions. Though the analysis reveals that entities greatly underutilize advanced cryptographic primitives such as zero-knowledge proofs and post-quantum hashing, these primitives potentially improve privacy and also verifiability. Present in the proposed model is a multi-layered architecture. Also, the model can offer a secure as well as transparent solution for addressing these gaps. Blockchain-based e-voting can increase trust, reduce fraud, and broaden democratic participation, but it requires real-world validation through pilot projects and usability testing.
Open access
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
Electronic voting (e-voting) has become an essential topic in the modernization of democratic systems, with promises of accessibility, faster counting, and reduced logistical challenges compared to traditional paper ballots. Yet, widespread adoption has been hindered by persistent trust and security concerns. Vulnerabilities such as malware, server compromise, insider threats, and limited verifiability have generated skepticism regarding the integrity of e-voting platforms. Blockchain technology has emerged as a disruptive innovation capable of reshaping this discourse. Its intrinsic properties—immutability, decentralization, transparency, and consensus-driven validation—directly address many of the fundamental challenges associated with securing digital elections. This manuscript provides a comprehensive exploration of blockchain-based electronic voting, with particular emphasis on the trust and security challenges that shape its practical deployment. Drawing on global case studies, theoretical models, and simulation insights, the research examines how blockchain can ensure tamper resistance, facilitate end-to-end verifiability, and empower voters through transparent audit trails. Key challenges such as scalability bottlenecks, voter anonymity risks, usability barriers, and regulatory gaps are analyzed in depth. The results indicate that hybrid blockchain architectures, which integrate advanced cryptographic techniques such as zero-knowledge proofs, homomorphic encryption, and sharding, hold promise for balancing the competing demands of scalability, privacy, and trust. Furthermore, blockchain must be supported by strong institutional frameworks, inclusive accessibility measures, and continuous technical audits to achieve legitimacy in electoral processes. By systematically mapping both the opportunities and limitations, this research contributes to the ongoing discourse on how technology can strengthen democratic resilience in the digital era. Ultimately, blockchain-enabled voting should be regarded not as a replacement but as an augmentation of existing systems, combining the strengths of distributed technologies with constitutional safeguards to advance secure, transparent, and inclusive electoral participation.
This study examines the design and deployment of scalable blockchain protocols that can serve as the backbone for smart city applications. The manuscript reviews existing blockchain consensus mechanisms—including Proof of Work (PoW), Proof of Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), and Proof of Authority (PoA)—and evaluates their suitability for heterogeneous smart city ecosystems. It further explores emerging scalability approaches such as sharding, sidechains, directed acyclic graphs (DAGs), and layer-2 protocols, alongside hybrid models that incorporate AI-driven optimization. A comparative simulation-based methodology is employed, assessing throughput, latency, and energy consumption across multiple blockchain prototypes. Results demonstrate that modular hybrid architectures leveraging sharding and DAG structures can increase throughput by up to 400% compared to traditional blockchains, with latency reductions of over 90% and significant energy savings. Beyond technical findings, the study contextualizes blockchain scalability within broader smart city governance frameworks, addressing interoperability between diverse urban domains such as energy microgrids, healthcare data platforms, autonomous mobility systems, and decentralized citizen services. The implications for data privacy, regulatory compliance, and citizen trust are also highlighted, emphasizing the necessity of balancing decentralization with governance oversight. By synthesizing technical, social, and policy considerations, this work contributes a comprehensive roadmap for scalable blockchain adoption in smart cities. Ultimately, the research demonstrates that with careful architectural design and integration of scalability-enhancing techniques, blockchain can evolve from a niche financial tool into a universal urban infrastructure enabler. The findings not only advance blockchain scalability research but also provide actionable insights for policymakers, urban planners, and technologists seeking to design sustainable, citizen-focused smart cities.
Blind signatures have become a cornerstone for privacy-sensitive applications such as digital cash, anonymous credentials, and electronic voting. The elliptic curve variant of the Digital Signature Algorithm (ECDSA) is widely adopted due to its efficiency in resource-constrained environments, such as mobile devices and blockchain systems. Building blind ECDSA is hence a natural goal. One presents the first such construction relying solely on the ECDSA assumption. Despite the inherent complexities in integrating blindness with ECDSA, we design a protocol that ensures both unforgeability and blindness without introducing new computational assumptions and ensuring concurrent security. It involves zero-knowledge proofs based on the MPC-in-the-head paradigm for complex statements combining relations on encrypted elliptic curve points, their coordinates, and discrete logarithms.
An anonymous credential (AC) system with partial disclosure allows users to prove possession of a credential issued by an issuer while selectively disclosing a subset of their attributes to a verifier in a privacy-preserving manner. In keyed-verification AC (KVAC) systems, the issuer and verifier share a secret key. Existing KVAC schemes rely on computationally expensive zero-knowledge proofs during credential presentation, with the presentation size growing linearly with the number of attributes. In this work, we propose two highly efficient KVAC constructions that eliminate the need for zero-knowledge proofs during the credential presentation and achieve constant-size presentations. Our first construction adapts the approach of Fuchsbauer, Hanser and Slamanig (JoC'19), which achieved constant-size credential presentation in a publicly verifiable setting using their proposed structure-preserving signatures on equivalence classes (SPS-EQ) and set commitment schemes, to the KVAC setting. We introduce structure-preserving message authentication codes on equivalence classes (SP-MAC-EQ) and designated-verifier set commitments (DVSC), resulting in a KVAC system with constant-size credentials (2 group elements) and presentations (5 group elements). To avoid the bilinear groups and pairing operations required by SP-MAC-EQ, our second construction uses a homomorphic MAC with a simplified DVSC. While this sacrifices constant-size credentials (n+2 group elements, where n is the number of attributes), it retains constant-size presentations (2 group elements) in a pairingless setting. We formally prove the security of both constructions and provide open-source implementation results demonstrating their practicality. We extensively benchmarked our KVAC protocols and, additionally, bechmarked the efficiency of our SP-MAC-EQ scheme against the original SPS-EQ scheme, showcasing significant performance improvements.
Jeongin Lee, Geunyeong Choi, Jihyo Han, Jungheum Park
Monero, a privacy-preserving cryptocurrency, employs advanced cryptographic techniques to obfuscate transaction participants and amounts, thereby achieving strong untraceability. However, digital forensic approach can still reveal sensitive information by examining off-chain artifacts such as memory and wallet files. In this work, we conduct an in-depth forensic analysis of Monero's wallet application, focusing on the handling of public and private keys and the wallet's data storage formats. We reveal how these keys are managed in memory and develop a memory scanning algorithm capable of identifying key-related data structures. Furthermore, we analyze the wallet keys and cache files, presenting a method for decrypting and interpreting serialized keys and transaction data encrypted with a user-specified passphrase. Our approach is implemented as an open-source Volatility3 plugin and a set of decryption scripts. Finally, we discuss the applicability of our methodology to multi-cryptocurrency wallets that incorporate Monero components, thereby validating the generalizability of our techniques.
The rapid progress of large-scale models, including foundational and generative, brings to the forefront the tension between data-driven innovation and core privacy concerns. Such contracts as the GDPR and the undue privacy threats of data aggregation make centralized training approaches less desirable. To analyze the data’s distributed characteristics and their application to FLO, we investigate the role of federation analytics in a plausible paradigm that shunts data. In this paper, we present a new federated learning (FL) framework enhanced with cutting-edge privacy technologies (PET) such as Differential privacy for user-level formal guarantees of confidentiality, and strengthened secure Multi-Party Computation (SMPC), which guards the model updates. This paper studies more recent approaches to resolving the principal challenges of FL: statistical heterogeneity, communication bottlenecks, and vulnerability to adversarial attacks. We greatly appreciate what this new method portends, especially for training large language models (LLMs) and the more delicate areas of healthcare and finance. By evaluating certain existing limitations, such as the complexities of federated fine- tuning and model fairness, it is clear that an architecture with exemplary performance in FL serves as a model for scalable, secure, and privacy cop.
Narendar Kumar, Surendar Kumar, Abdul Waqar, Clavincy Francis Yohanes Ngantung
This research article provides the design of an in-person and remote voting system, while at the same time ensuring the privacy of users that would guarantee openness, transparency, and at the same time fraud-free results. The aim is to solve various common problems associated with most conventional elections including fraud, vote manipulation, through adaptation of the usage of a safe, highly transparent decentralized logical Hyperledger Fabric-based system provided by blockchain implementation. The methodology in this article is to be implemented for the sheer reason of urgency needed in making a more secure and transparent system for voting, considering even the rising frauds in elections. The addition of Zero Knowledge Proof (ZKP) guarantees that votes are confident and correct, yet anonymous between a voter and their vote. Biometric identification makes the system resistant to double spending. This incorporation of technologies ensures there is privacy and immutability against the double transactions, which, in turn, would be put in place as foundation for the future to be provided wherein every process in an election becomes safe and transparent. Innovation via creating a voting system to be trusted to meet today's demands and set standards for future electoral processes.
The evolution of technology has sparked significant interest in transforming traditional voting into efficient, secure online systems.This study introduces a novel approach that enhances voter privacy and data security by utilizing a UniqueBlend ID algorithm to generate unique identifiers for voters, obscuring Aadhar numbers and preventing identity disclosure.Blockchain technology is integrated to enhance transparency, eliminate fraud, and create an immutable voting record.However, integrating decentralized applications (dApps) with legacy web2 systems presents challenges in data storage and retrieval.To address these issues, this research presents Optima, an interface that simplifies data segregation between web2 and web3 storage systems using a JSON-based structure.Optima optimizes storage efficiency, minimizes gas fees, and reduces development overhead, allowing developers to focus on application logic.This streamlined data segregation approach significantly improves the efficiency and security of online voting, ensuring voter anonymity and maintaining the integrity of the voting process.
In traditional, one-vote-per-person voting systems, privacy equates with ballot secrecy: voting tallies are published, but individual voters' choices are concealed. Voting systems that weight votes in proportion to token holdings, though, are now prevalent in cryptocurrency and web3 systems. We show that these weighted-voting systems overturn existing notions of voter privacy. Our experiments demonstrate that even with secret ballots, publishing raw tallies often reveals voters' choices. Weighted voting thus requires a new framework for privacy. We introduce a notion called B-privacy whose basis is bribery, a key problem in voting systems today. B-privacy captures the economic cost to an adversary of bribing voters based on revealed voting tallies. We propose a mechanism to boost B-privacy by noising voting tallies. We prove bounds on its tradeoff between B-privacy and transparency, meaning reported-tally accuracy. Analyzing 3,582 proposals across 30 Decentralized Autonomous Organizations (DAOs), we find that the prevalence of large voters ("whales") limits the effectiveness of any B-Privacy-enhancing technique. However, our mechanism proves to be effective in cases without extreme voting weight concentration: among proposals requiring coalitions of $\geq5$ voters to flip outcomes, our mechanism raises B-privacy by a geometric mean factor of $4.1\times$. Our work offers the first principled guidance on transparency-privacy tradeoffs in weighted-voting systems, complementing existing approaches that focus on ballot secrecy and revealing fundamental constraints that voting weight concentration imposes on privacy mechanisms.
This paper proposes a hybrid access control system that integrates the usability of Web2 authentication (Google Login) with the transparency and integrity of Web3 technologies (blockchain and smart contracts). The system enables users to authenticate via their existing Google accounts without managing crypto wallets or private keys, while access permissions are securely recorded on-chain through smart contracts. To ensure cryptographic key security without relying on a centralized authority, the design incorporates Distributed Key Management (DKM). This approach addresses the challenge of balancing usability with verifiability in data access control. By embedding decentralized guarantees within a centralized web service interface, the system enables practical and transparent access control. The proposed architecture demonstrates the potential for a general-purpose, auditable module that facilitates user-consented data sharing with third parties.
Integration of Federated Learning (FL) with Blockchain technology to decentralized privacy-preserving, and scalable framework for strengthening cybersecurity. As cyber threats like ransomware, malware, and network intrusions grow in complexity, there is an increasing need for collaborative threat detection and mitigation. However, traditional collaborative approaches often involve sharing sensitive information across organizations, raising significant privacy concerns and regulatory challenges under frameworks like GDPR and HIPAA. FL works to solve these problems through enabling multiple entities to work together on training machine learning models without sharing their original information. Despite its advantages, FL faces challenges such as the risk of model tampering, trust deficits between participants, and dependence on a centralized server for model aggregation. To overcome these limitations the Blockchain technologies will be in used so blockchain technology provides a distributed, transparent, and non-mutable ledger that safely manages FL operations. It helps preserve the accuracy and trustworthiness of model updates via smart contracts along with consensus mechanisms, bypassing the requirement fora central aggregator. In addition, blockchain enables incentivization by introducing token-based rewards, encouraging active participation in collaborative threat detection networks. Privacy- preserving techniques to boost information security, techniques like differential privacy and homomorphic encryption are also put into practice. Such a integration of FL and blockchain is particularly impactful in securing distributed systems such as IoT devices, critical infrastructure, and enterprise networks, where privacy, trust, and scalability are crucial. This project aims to demonstrate the practical implementation of this framework, paving the way for adaptive and globally scalable cyber security systems to combat evolving threats.
David Chaum, Richard Carback, Jeremy Clark, Liu Chao · 11 authors
Abstract We revisit “silent coercion” where an adversary gains access to a voter’s credential without the voter’s knowledge in an E2E verifiable, coercion-resistant Internet voting system. We argue that in this setting, casting an intended vote is impossible since the cryptographic backend can no longer distinguish the voter and adversary. However, we affirm that the voter can still act to nullify adversarial ballots, which is preferable to inaction. We provide a new instantiation of nullification using zero-knowledge proofs and multiparty computation, which improves on the efficiency of the current state-of-the-art. We also demonstrate an example voting system—VoteXX—that uses nullification. Our nullification protocol can complement new and existing techniques for coercion resistance (which all require voters to hide cryptographic keys from the coercer), providing a failsafe option for voters whose keys leak.