We study distributed zero-knowledge proofs, introduced by Bick, Kol, and Oshman (SODA 2022). While distributed interactive proofs have advanced rapidly, general-purpose techniques for distributed zero-knowledge remain limited and mostly problem-specific. We address this gap by introducing distributed statistical zero-knowledge, requiring that each node's view be simulatable within negligible statistical distance, and by lifting the classical Sumcheck protocol (Lund, Fortnow, Karloff, and Nisan, FOCS 1990) into a modular primitive for distributed zero-knowledge proofs. Our main contribution is a distributed zero-knowledge implementation of Sumcheck. Given oracle access to a polynomial F over a finite field $\mathbb{F}$ with N variables, we design a protocol verifying claims of the form $\sum_{x\in\mathbb{F}} F(x)=a$ using $O(N)$ rounds of $O(\log |\mathbb{F}|)$-bit messages, while achieving statistical zero-knowledge and small soundness error. We apply this primitive to two problems. For non-k-colorability, we obtain an $O(n)$-round distributed statistical zero-knowledge proof deciding whether a graph is not k-colorable, for any constant k, using $O(log^{1+o(1)} n)$-bit messages. This is the first nontrivial distributed interactive proof for this problem, even without zero-knowledge guarantees. For Subgraph Counting, we obtain an $O(k \log n)$-round, $O(k \log n)$-bit distributed statistical zero-knowledge proof for counting copies of a given k-node pattern, improving previous distributed interactive proofs while additionally providing statistical zero-knowledge. Finally, we show that additional round compression of Sumcheck is problem-dependent: for non-3-colorability on constant-degree graphs, we prove a lower bound excluding $o(n/\log n)$ rounds under polynomial-time local computation.
Proof of Stake (PoS) blockchain systems require weighted threshold signatures where participants' voting powers reflect their stakes. As stakes change dynamically through deposits and withdrawals, efficient weight and threshold adjustments are essential for maintaining system security and availability without downtime. However, existing approaches face critical limitations: (1) virtualization-based schemes require$O(w)$operations (signatures) per participant with weight$w$; (2) dynamic threshold / paricipants schemes do not support weighted participants; (3) schemes with both properties require trusted dealers or$O(n^{2})$re-setup, causing temporary unavailability. This paper introduces Active Weighted Signature (AWS), enabling dynamic adjustments without trusted dealers or re-setup. We propose Generalized Lagrange Interpolation (GLI), encoding weights as polynomial derivatives rather than virtualized participants, and instantiate AWS through$ {\sf ADAPT}$by applying GLI to the Schnorr-based threshold signature${\sf FROST}$. Our implementation shows that$ {\sf ADAPT}$achieves comparable efficiency to${\sf FROST}$for key generation, while weight and threshold adjustments complete in 4.1-22.3% of re-setup time. For uneven weight distributions,$ {\sf ADAPT}$achieves sub-linear scaling: 49× weight difference requires only 3.29× computation versus 49× in virtualization.
With the widespread adoption of cryptocurrencies, the ability to conduct continuous offline payments has increasingly become a critical technological requirement. In network-constrained scenarios, current dual-offline payment technologies are useful for single transactions. However, their limitations in continuous payment scenarios have become increasingly evident, making them unable to meet real-world application needs. This has prompted the industry to demand more urgent innovations in research on continuous offline payment capabilities. To address these challenges, this paper proposes a continuous dual-offline payment system capable of supporting multiple continuous payments. The system integrates elliptic curve cryptography (ECC) and zero-knowledge proof (ZKP) technology to generate secure asset credentials, ensuring both immutability and privacy credentials throughout the offline payment lifecycle. A dynamic credential decomposition mechanism enables the splitting of input credentials into change credentials and receipt credentials, facilitating uninterrupted dual-offline payments between hardware wallets. Additionally, it incorporates a batch verification scheme based on smart contracts, utilizing zero-balance verification and chained hash tracing to ensure payment uniqueness and prevent double-spending attacks, thereby guaranteeing the verifiability and validity of payment settlements. Experimental evaluations demonstrate that the proposed system reduces gas consumption per payment and improves execution efficiency during batch processing, combining high security with strong performance. This research provides a feasible solution for the application of digital currencies in offline scenarios, carrying significant theoretical value and practical significance for driving technological innovation and application expansion in the cryptocurrency field. In addition to cryptocurrency payments, the proposed system is also applicable to IoT and sensor network environments. Many IoT devices operate in disconnected or network-limited areas and require secure micro-transactions. Our dual-offline payment mechanism supports such scenarios, as the main cryptographic operations are lightweight enough for typical IoT hardware. This further extends the practical value of our system beyond traditional cryptocurrency payments.
Vote Chain is a fully implemented, decentralized e-voting application (DApp) built on Ethereum. Existing blockchain-based voting systems often suffer from either high computational overhead due to homomorphic encryption or lack of fully deployable, adversarially tested implementations. To address these limitations, VoteChain employs a keccak256-based commit–reveal protocol to preserve ballot secrecy during the voting phase, with Solidity 0.8.20 smart contracts enforcing all election rules autonomously. Wallet-based authentication via MetaMask eliminates centralized identity management. The system is validated through 14 automated unit tests (all passing in 615 ms) covering correctness, access control, double-voting, hash forgery, and phase-bypass attacks. Per-voter gas cost is approximately 120,000 units (commit and reveal combined). An ablation study confirms the non-redundant contribution of each architectural component. Comparative analysis shows that VoteChain achieves vote privacy without homomorphic encryption while maintaining full decentralization and implementation completeness. The system is evaluated and validated on a local Hardhat network, with the architecture readily extensible to Layer-2 rollups for large-scale elections.
Federated Learning (FL) enables collaborative model training while preserving data privacy but relies on centralized aggregation servers, leading to issues such as lack of transparency, vulnerability to malicious updates, and single points of failure. This paper proposes a decentralized federated learning framework integrating blockchain technology and the InterPlanetary File System (IPFS) to eliminate central authority and enhance trust. Smart contracts deployed on the Ethereum Sepolia testnet manage model submission, validation, incentive distribution, and reputation tracking. Model updates are stored off-chain using IPFS, while their hashes are recorded on the blockchain to ensure integrity and immutability. A staking and slashing mechanism is introduced to encourage honest participation, where valid contributions are rewarded and malicious updates are penalized. A reputation system further evaluates participant reliability over time. The system is implemented using PyTorch, Solidity, Web3.py, and React.js. Experimental results demonstrate improved security, transparency, and efficient decentralized coordination, highlighting the feasibility of integrating federated learning with blockchain and decentralized storage for scalable and trustworthy machine learning applications.
Umar . Abubakar, Hamza Itopa Sanni, Abdulsalam Aliyu
Conventional attendance management approaches in academic and organisational settings face persistent challenges that include susceptibility to proxy attendance, inadequate protection of biometric credentials, and the absence of privacy-preserving verification mechanisms in cloud-hosted deployments. This paper presents SecureAttend, a cloud-based attendance management framework that addresses these deficiencies through the integration of Zero-Knowledge Proof (ZKP) cryptographic authentication with biometric capture via a ZKTeco K40 Pro fingerprint terminal. The proposed framework employs a challenge-response ZKP protocol that enables users to demonstrate possession of valid authentication credentials without disclosing underlying private keys or biometric templates to the server. Attendance records are encrypted using AES-256 prior to storage in a MongoDB cloud database, while SHA-256 hashing provides tamper-evidence for each record. Session integrity is maintained through JWT-based token management, and access boundaries are enforced via a Role-Based Access Control (RBAC) policy. Functional evaluation across eighteen test scenarios confirmed complete compliance with stated requirements. Security assessment validated correct operation of cryptographic mechanisms, access controls, and audit logging subsystems. Performance benchmarks recorded average API response latencies of approximately 85 milliseconds for authentication requests and 120 milliseconds for attendance marking operations. The results demonstrate that ZKP authentication can be deployed effectively in real-world attendance management contexts, offering measurable improvements in privacy, integrity, and resistance to credential-based attacks compared with conventional approaches.
Cryptography is the backbone of modern network security, providing confidentiality, integrity, authentication, and non-repudiation for digital communication. However, the rapid evolution of cyber threats, particularly the looming arrival of large-scale quantum computers, poses serious challenges to the cryptographic algorithms that protect today's networks. This paper presents a systematic review of cryptography in network security, following the PRISMA 2020 guidelines. A total of 68 studies published between 2016 and 2025 were selected from five major academic databases: IEEE Xplore, ACM Digital Library, Scopus, Web of Science, and ScienceDirect. The review covers classical symmetric and asymmetric algorithms, widely deployed cryptographic protocols such as TLS 1.3, IPsec, and SSH, and the growing body of work on post-quantum cryptography (PQC). Key findings include the following: NIST finalized three post-quantum cryptographic standards (FIPS 203, 204, and 205) in August 2024; lightweight cryptography standards for IoT devices were published in 2025 with the selection of ASCON; and real-world deployment of hybrid classical/post-quantum schemes has already begun in major web browsers and messaging applications. This paper also examines emerging trends in homomorphic encryption, zero-knowledge proofs, and AI-driven cryptanalysis. Based on the findings, this review identifies critical gaps in PQC migration strategies, IoT security, and the integration of cryptography with artificial intelligence, and proposes directions for future research.
Swati Sachan, Dale Fickett, Richard Buchinger, Theo Miller
Recent advances in error-corrected qubits have accelerated the timeline for practical quantum computing. It poses a threat to cryptographic primitives used to secure financial systems, government infrastructure, communication networks, and DeFi (Decentralized Finance) ecosystems. This paper introduces a post-quantum secure federated DeFi framework that enables inter-bank collaboration to improve the inclusivity of individuals underserved by local lenders due to limited financial histories. Multiple banks contribute encrypted information batches to a virtual server, where lattice-based Fully Homomorphic Encryption (FHE) enables end-to-end homomorphic computation. The server fuses local data-driven probabilistic assessments, expert beliefs, and verifiable evidence generated by the NASA-IBM Prithvi Geospatial Foundation Model (GFM), in encrypted format. Decentralized technologies are employed to ensure tamper-proof evidence and auditable accountability for all encrypted data exchanges between institutions and the server. The framework is tested on agricultural lending decisions for rural borrowers in Virginia.
In this paper, we examine the patterns and determinants of cross-border cryptocurrency flows. While our analysis focuses primarily on Bitcoin flows, the cryptocurrency with the largest market capitalization, we show that our key results also extend to four major stablecoins. After documenting global patterns of cross-border Bitcoin flows and contrasting them with those of traditional capital flows, we employ a cross-country panel approach to identify the key drivers of cross-border crypto flows for up to 162 countries. Our results provide evidence for the presence of multiple coexisting motives. The most significant motives comprise strategies to adjust to unfavorable macro and financial developments, as well as the need to conduct international payment and remittance transfers. Moreover, by conducting a case study of cross-border Bitcoin flows after the COVID-19 shock, we find that these motives were particularly relevant at a time when economic conditions were weak and the need for remittances appeared high. Gaining a better understanding of the motives behind cross-border cryptocurrency transactions is crucial for informing the public debate on cryptocurrencies and their potential use cases.
A non-custodial threshold instrument for Bitcoin would allow value to transfer between parties without network connectivity, fees, or custodial dependency. Digital signatures and multisignature scripts provide part of the solution, but the core benefit is lost if the issuer retains a key capable of unilateral redemption. All prior multisignature schemes have positioned the issuer at or above the spending threshold. We propose a system that inverts this: the holder receives the two keys constituting the spending threshold of a 2-of-3 multisignature script, and the issuer holds one key arithmetically below it.
The conventional online recruitment systems depend heavily on centralized systems which often create problems in transparency, resume fraud, data monopoly, and low levels of trust between employers and applicants. As a solution to such shortcomings, this paper considers a blockchain-based decentralized recruitment system, combining verifiable credentials, decentralized identity (DID), and hiring technology that respects privacy. The system suggested uses smart contracts to make the recruitment process a fully automated procedure (job posting, application, credential verification, and hiring decisions) that does not rely on centralized intermediaries. Candidate credentials (including education, certifications, and work experience) are cryptographically signed by authorized issuers, and will be stored as hashes on the blockchain, which cannot be altered, whereas documentation is stored in the decentralized storage (IPFS). To enhance privacy, the platform uses selective disclosure and zero-knowledge proofs (ZKP) features that also allow applicants to reveal their knowledge of skills without revealing sensitive personal data. The architecture also introduces DAO-based governance and score of reputation to increase responsibility of employers, credit issuers and applicants. Experimental testing using synthetic recruitment data proves that it has higher reliability in recruiting, with 91% accuracy in catching resume fraud compared to 78% with conventional applicant tracking system and has reasonable transaction latency and growth capability with a hybrid on-chain off-chain architecture. The findings indicate that the use of blockchain in the recruitment systems can significantly positively contribute to the transparency, authenticity of credentials, and trust in the modern hiring ecosystems.
Paul Gerhart, Jay Taylor, Sri Aravinda Krishnan Thyagarajan
Atomic swaps are a fundamental primitive for the trustless exchange of digital assets across blockchains: they guarantee that either both parties receive the agreed assets or neither party transfers. While this all-or-nothing guarantee is powerful, it also imposes an inherent determinism that rules out exchanges whose intended outcome is probabilistic. As a result, existing atomic swaps cannot realize trustless exchanges in which one party pays for a fixed chance of receiving a larger asset or reward, as in lotteries, randomized allocation mechanisms, and probabilistic cross-chain trades. We introduce probabilistic swaps, a new cryptographic primitive that extends atomic swaps to the probabilistic setting. In a probabilistic swap, one party's transfer is executed with a fixed, publicly specified probability embedded in the protocol and cannot be biased by either party. This yields a trustless mechanism for randomized exchange with verifiable odds and no trusted intermediary. Our construction combines adaptor signatures with oblivious pseudorandom functions (OPRFs) to realize the desired probabilistic outcome while ensuring that neither party can predict or bias it in advance. Along the way, we introduce a new mechanism for the atomic exchange of OPRF evaluations for payments, which may be of independent interest. A key feature of our approach is that it preserves the minimal on-chain footprint of modern atomic-swap protocols. The protocol relies only on standard Bitcoin scripts, such as digital signatures and timelocks, and is deployable on any blockchain that already supports atomic swaps. Consequently, probabilistic swaps are indistinguishable from ordinary on-chain transactions, which helps preserve privacy and fungibility. We provide formal security foundations and demonstrate practicality through a probabilistic swap in the Bitcoin testnet and in the Lightning Network.
Data valuation is a foundational task in data marketplaces, where a Shapley-value attribution determines how a buyer's payment is distributed among data providers. Typically, the marketplace operator runs this attribution alone, requiring participants and external auditors to trust scores they cannot independently recompute on the underlying private data. While zero-knowledge proofs (ZKPs) can theoretically reconcile this conflict between privacy and verifiability, existing ZK valuation systems fail to scale to real-world marketplace demands due to prohibitive proving times or the requirement to disclose validation cohorts. We present ZK-Value, a practical, end-to-end ZK data-valuation system. Our solution bridges the scalability gap through a fully co-designed architecture: (1) LSH-Shapley, a locality-based valuation primitive that replaces expensive pairwise distance metrics with per-bucket collision counts; (2) ZK-LSH-Shapley, a tailored ZKP protocol that drastically reduces witness size by encoding these counts into bucket-level histograms rather than naive per-pair tensors; and (3) structural proof-system optimizations, specifically super-oracle batching and sparsity skipping. Evaluated across 12 standard datasets, ZK-Value delivers valuation quality on par with state-of-the-art baselines (within 0.033 AUROC of exact KNN-Shapley), while generating proofs in seconds to minutes and outperforming specialized ZK baselines by 12.6x to 68.1x in proving time, with verification in under 4.6 s.
Sinchana Shetty, Tejaswini M R, Kiran Samantha D S, Vijaylaxmi H Manjunatha
Existing electronic voting platforms are persistently centralized repositories, introducing fundamental security challenged by vote manipulation, result falsification, limited weaknesses [1]. Blockchain technology has emerged as a compelling alternative, owing to its cryptographic permanence, data management. This paper proposes and evaluates a fully integrated blockchain-based electoral system built on the Ethereum network, leveraging Solidity smart contracts to address these systemic shortcomings. The proposed architecture adopts a decentralized three-tier design incorporating Web3.js communication bridges and cryptographic validation mechanisms that collectively guarantee immutability, transparency, and end-to-end verifiability throughout all electoral phases. The system incorporates hierarchical role-based access controls, real-time vote tallying, and comprehensive audit trail functionality, while preserving voter anonymity through pseudonymous addressing. Experimental results demonstrate transaction confirmation within 15–20 seconds, with a mean gas consumption of 0.0023 ETH per vote, confirming practical feasibility for medium-scale deployments. A comparative evaluation against conventional centralized e-voting solutions highlights measurable security full-stack Ethereum-based voting platform comprising Solidity improvements and the elimination of single points of failure, balanced against acceptable computational overhead.
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Biometric Identification and Security
Advanced Steganography and Watermarking Techniques
Anonymous digital credentials allow a user to prove possession of an attribute that has been asserted by an identity issuer without the user revealing any extra information about themselves. For example, a user who has received a digital passport credential can prove their “age is <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>></mml:mo> <mml:mn>18</mml:mn> </mml:mrow> </mml:math> ” without revealing any other attributes such as their name or date of birth. Despite their clear application to privacy-preserving authentication, anonymous credential schemes have been difficult to deploy at scale. Part of the difficulty arises because schemes in the literature, such as BBS+, use new cryptographic primitives that require system-wide changes to existing issuer infrastructure. In addition, issuers often require digital identity credentials to be device-bound by incorporating the device’s secure element into the presentation flow. As a result, schemes like BBS+ require updates to the hardware on every user's device. We propose new ZK techniques which enable the construction of an anonymous credential scheme for the legacy Elliptic Curve Digital Signature Algorithm (ECDSA) signature scheme. By adding efficient ZK arguments for statements about SHA-256 and document parsing for ISO-standardized identity formats, we construct the first ZK proof of posession of a credential that can be deployed without changing any issuer processes, without changes to mobile devices, and without requiring non-standard cryptographic assumptions. Furthermore, our proof system itself only relies on SHA-256 as its complexity assumption. Producing ZK proofs about ECDSA signatures has been a bottleneck for other ZK proof systems because standardized curves such as P256 use finite fields which do not support efficient number theoretic transforms. We overcome this bottleneck by designing a ZK proof system around sumcheck and the Ligero argument system, by designing efficient methods for Reed-Solomon encoding over the required fields, and by designing specialized circuits for ECDSA. Our proofs for ECDSA can be generated in as little as <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>≈</mml:mo> <mml:mn>20</mml:mn> </mml:mrow> </mml:math> ms. When incorporated into a fully standardized identity protocol such as the ISO MDOC standard, our system can generate a zero-knowledge proof for the MDOC presentation flow in a few hundred ms on mobile devices. These advantages make our scheme a promising candidate for privacy-preserving digital identity applications.
The intersection of Artificial Intelligence (AI) and distributed systems has given rise to Federated Learning (FL), a paradigm that enables decentralized model training without compromising local data privacy. As organizational data silos grow, deploying complex machine learning models across highly distributed edge networks becomes a critical infrastructural challenge. Standard FL implementations suffer from severe vulnerabilities related to adversarial gradient updates and computational bottlenecks at the aggregation layer. This paper presents a novel, end-to-end distributed architecture that hardens FL pipelines using advanced cryptographic verification and optimized big data processing frameworks. We introduce a Zero-Knowledge Proof (ZKP) wrapper that cryptographically validates node computations before global aggregation, neutralizing model poisoning attacks without inspecting raw gradients. Additionally, we evaluate the system's performance using extreme gradient boosting models optimized for distributed edge execution. We formalize the mathematical transformation of the machine learning loss functions into Rank-1 Constraint Systems (R1CS) suitable for succinct verification. Extensive experimental results demonstrate that our hybrid architecture achieves a 94.2\% accuracy retention under adversarial conditions while maintaining scalable throughput across 1,000 parallel distributed nodes, effectively bridging the gap between rigorous cryptographic security and high-performance distributed AI.
While public blockchains provide transparent and auditable transaction histories, they inherently compromise user privacy. Existing privacy-enhancing protocols, such as those deployed on Ethereum, typically rely on succinct zero-knowledge proofs (zk-SNARKs) to obscure the transaction graph. However, implementing comparable cryptographic guarantees on high-throughput blockchains like Algorand is challenging due to strict per-call execution budgets and the state contention introduced by global Merkle accumulators. This paper presents Obscura, a decentralized, non-custodial privacy protocol tailored for constrained smart contract environments. Obscura achieves transaction anonymity using Linkable Spontaneous Anonymous Group (LSAG) signatures over the BN254 elliptic curve, verified entirely on-chain. To overcome limitations of the Algorand Virtual Machine (AVM), we introduce a novel state model that leverages Algorand's Box Storage for $O(1)$ commitment membership checks, eliminating the need for global Merkle accumulators, and a dynamic opcode-budget expansion mechanism via pooled inner application calls. Our implementation demonstrates that signer-ambiguous privacy is practical and efficient on Algorand without relying on trusted setups or succinct proofs. Obscura provides a robust privacy layer for transparent ledgers, bridging the gap between high-throughput blockchain architectures and the dual requirements of cryptographic privacy and selective auditability.
Dr. P. U. Anitha, G.Rachana, K. Sushmitha, A. V. Senthil Kumar · 5 authors
Electronic Know Your Customer (e-KYC) systems play a crucial role in verifying user identities for financial institutions, telecom operators, and other service providers. However, traditional eKYC frameworks rely heavily on centralized databases, making them vulnerable to data breaches, unauthorized access, single points of failure, and privacy risks. To address these challenges, this paper proposes a blockchain-based e-KYC system that enhances trust, security, and user privacy. The proposed architecture integrates distributed ledger technology with smart contracts to create a transparent, tamperproof, and decentralized identity verification platform. User data is encrypted and stored securely, while verification records are maintained on an immutable blockchain ledger. Authorized entities can access customer information only through permissioned smart contracts, ensuring controlled data sharing and minimizing exposure. This approach eliminates repeated KYC processes, reduces operational costs, prevents identity fraud, and strengthens user control over personal data.Over all, the blockchainenabled e-KYC system provides a more reliable, efficient, and privacy-preserving solution compared to conventional centralized models. It significantly improves trust among stakeholders and establishes a secure foundation for digital identity management in modern financial and governmental ecosystems.
Electronic voting systems are designed to modernise democratic processes by providing efficient, transparent, and accessible elections. They promise rapid tallying, reduced human error, and increased participation. However, large-scale deployment remains hindered by unresolved concerns around security, privacy, scalability, and trust. Direct recording electronic machines suffer from limited verifiability. In Internet voting, the more fundamental risks stem from compromised end-user devices, difficulty in achieving end-to-end verifiability that ordinary voters can meaningfully check, and exposure to coercion and vote buying, rather than generic cyberattacks alone. Blockchain-based approaches, while offering immutability and public auditability, face significant scalability limits. Network throughput and latency constrain election-sized workloads, fee volatility creates cost uncertainty, and per-vote on-chain actions, such as posting ballots and verifying cryptographic proofs, incur prohibitive costs that grow linearly with the number of voters. The openness of public ledgers also leaks metadata that can erode privacy. Consequently, achieving accountability and transparency without sacrificing individual ballot secrecy and practical efficiency remains a central challenge. This thesis addresses these challenges by providing the first thorough analysis of a ranked-choice blockchain election protocol that had not been previously examined in detail. The analysis identifies critical limitations related to privacy and efficiency that weaken unlinkability and verifiability. Building on these findings, the first significant contribution of this thesis is the design of a new, verifiable, and privacy-preserving voting framework that eliminates the need for a trusted tallying authority. The scheme employs anonymous participation tokens and nullifiers to enforce one-time eligibility. At the same time, voters cast encrypted ballots accompanied by zero-knowledge proofs that confirm eligibility, correctness, and uniqueness without disclosing their identity or the content of their vote. Tallying is achieved through a decentralised threshold decryption process that ensures fairness and confidentiality. The second significant contribution of this thesis is an efficient proof-batching and verification mechanism that addresses the scalability bottlenecks of existing blockchain-based approaches. By aggregating multiple proofs off-chain and submitting a single, succinct batch proof with updated commitments, the protocol reduces on-chain verification to a near-constant cost per voter. This significantly decreases computational and financial overhead, enabling scalability to large elections. The proposed framework is analysed under realistic adversarial models and shown to achieve privacy, eligibility, uniqueness, and universal verifiability. Performance evaluation demonstrates that the batching mechanism substantially lowers costs compared to existing protocols. Overall, this research advances the state of the art in blockchain-based electronic voting by providing the first comprehensive evaluation of a ranked-choice protocol and proposing novel mechanisms that overcome its limitations, thereby laying a foundation for secure, transparent, and scalable digital elections.