N.Shanmuga Priya, M. A. Gopinath, V Gunaseelan., K Guruganesh
This paper presents a new decentralized e-voting architecture designed to create tamper-proof, transparent, and secure election systems using the blockchain and smart contracts. The solution uses a permissioned blockchain structure that is private and Hyperledger Fabric-based in an effort to improve scalability and privacy while retaining the inherent features of decentralization. A zero-knowledge proof (ZKPs) based novel authentication method is implemented to provide eligibility checking and safeguard the privacy of the voter. The smart contracts are aimed at automating ballot tallying, publishing results, and checking their validity. The system is also resistant to traditional channels of attack such as denial-of-service, vote tampering, and voting redundancies based on its distributed ledger and consensus algorithms. A light-weight online interface has been implemented in an effort to promote usability and accessibility, thereby showing an unproblematic voter experience. Experimental results have now made it feasible to deploy the system in organizational and government voting applications, thereby testing its effectiveness within these settings. This method represents a tangible step towards an entirely reliable digital democracy.
Zero-Knowledge Proofs (ZKP) are protocols which construct cryptographic proofs to demonstrate knowledge of a secret input in a computation without revealing any information about the secret. ZKPs enable novel applications in private and verifiable computing such as anonymized cryptocurrencies and blockchain scaling and have seen adoption in several real-world systems. Prior work has accelerated ZKPs on GPUs by leveraging the inherent parallelism in core computation kernels like Multi-Scalar Multiplication (MSM). However, we find that a systematic characterization of execution bottlenecks in ZKPs, as well as their scalability on modern GPU architectures, is missing in the literature. This paper presents ZKProphet, a comprehensive performance study of Zero-Knowledge Proofs on GPUs. Following massive speedups of MSM, we find that ZKPs are bottlenecked by kernels like Number-Theoretic Transform (NTT), as they account for up to 90% of the proof generation latency on GPUs when paired with optimized MSM implementations. Available NTT implementations under-utilize GPU compute resources and often do not employ architectural features like asynchronous compute and memory operations. We observe that the arithmetic operations underlying ZKPs execute exclusively on the GPU's 32-bit integer pipeline and exhibit limited instruction-level parallelism due to data dependencies. Their performance is thus limited by the available integer compute units. While one way to scale the performance of ZKPs is adding more compute units, we discuss how runtime parameter tuning for optimizations like precomputed inputs and alternative data representations can extract additional speedup. With this work, we provide the ZKP community a roadmap to scale performance on GPUs and construct definitive GPU-accelerated ZKPs for their application requirements and available hardware resources.
Real-World Assets (RWAs) serve as a bridge between traditional financial instruments and decentralized infrastructures. By representing assets such as bonds, commodities, and real estate on blockchains, RWAs can extend the scope of decentralized finance. Industry forecasts further indicate rapid growth in tokenized RWAs after 2025, underscoring their potential role in the evolution of digital financial markets. However, in the current multi-chain environment, RWAs face challenges such as repeated authentication across multiple chains and inefficiencies arising from multi-step settlement protocols. To address these issues, we present a cross-chain framework for RWAs that emphasizes identity management, authentication, and cross-chain interaction. The framework integrates Decentralized Identifiers and Verifiable Credentials with customized attributes to support decentralized identification, and incorporates an authentication protocol based on Simplified Payment Verification to avoid redundant verification across chains. Furthermore, this paper adopts a cross-chain channel that supports efficient RWA settlements, and we refine its design so that the channel does not need to be closed immediately after each settlement, thereby reducing on-chain cost. We implement the framework and evaluate its performance via simulations, which confirm its feasibility and demonstrate improvements in efficiency for RWAs in cross-chain settings.
Web3 has attracted considerable attention in fields including DeFi, DApps, and NFTs due to its decentralization, enhanced privacy, and user-centricity. However, interoperability and scalability challenges hinder its widespread adoption. While deploying anonymous credentials across Web3 networks to enable cross-network service access is a potential solution to these challenges, existing credential systems remain limited by centralized management, high energy consumption, and credential abuse, making them unsuitable for Web3 environments. To overcome these limitations, we propose a decentralized anonymous functional credential (DAFC) scheme that is efficient, privacy-preserving, and linkable. Unlike existing schemes, DAFC enables users to generate a single proof embedding attributes$x$for requesting services under different access policies. Each provider can use the functional key$sk_{F}$associated with their respective access policy$F$to extract$F(x)$for attribute verification. This significantly reduces authentication computational overhead. Furthermore, DAFC's linkability effectively mitigates credential abuse risks. As an additional contribution, we propose a novel construction of non-interactive zero-knowledge functional proof (fNIZK) based on one-out-of-many proofs and functional encryption for inner products, which is the building block of DAFC. Security analysis demonstrates that DAFC achieves anonymity, unforgeability, and linkability. Performance evaluation shows that DAFC outperforms prior schemes in both computational and communication overhead when requesting at least 6 services with distinct access policies.
Pablo Sotres, Alberto Carelli, M Festa, Maxime Costalonga · 9 authors
The secure and trustworthy exchange of interoperable data assets is a key enabler for the development of IoT-based data spaces. This paper presents a decentralised framework for offering management and asset sharing within trusted and interoperable data spaces, leveraging Distributed Ledger Technologies (DLTs) and the Self-Sovereign Identity (SSI) paradigm to ensure transparency, integrity, and participant self-sovereignty. The proposed architecture integrates identity management and tamper-resistant smart contracts leveraging the IOTA Tangle to support decentralised offering discovery, access control and verifiable transactions. By addressing critical challenges related to trust, interoperability, and decentralisation, the framework contributes to the technological foundations required for scalable, secure, and resilient data ecosystems.
Gabriel Babatunde Iwasokun, Oluwaseyi Segun, Samuel Oluwatayo Ogunlana, Michael Adegoke · 6 authors
The integration of Internet of Things (IoT) devices into modern payment systems has introduced innovative functionalities, but also significant security and performance challenges. IoT devices, such as smart sensors, wearables, and automated vending machines, are typically resource-constrained yet handle sensitive financial transactions that demand robust security mechanisms. Conventional cryptographic solutions are often unsuitable for these environments due to their high computational and memory requirements. This paper presents the design of a lightweight blockchain-based model to secure IoT payment systems by leveraging the Ethereum blockchain and AES-128 encryption. The blockchain token is encrypted with AES-128 to add layer of security before being stored in a database. The model is designed to employ a decentralised digital ledger to record and validate transactions without a central authority, and the transaction is grouped into a block and linked to the preceding block through cryptographic hashes. The chain of blocks forms an immutable record that enhances transparency and security, and the distributed nature of blockchain networks, wherein multiple participants validate each transaction, minimises the risk of fraudulent activities while ensuring consensus is achieved through predefined protocols. Analysis of results from the implementation established the minimization of computational overhead and robust security measures, and was particularly beneficial where the scalability of decentralized systems is required alongside heightened security protocols.
Transport Infrastructure Ireland (TII) commissioned a cooperative intelligent transport system (C-ITS) pilot. Although the European C-ITS Security Credential Management System (EU CCMS) and European Telecommunications Standards Institute Public Key Infrastructure (ETSI PKI) standards offer a foundation for secure Vehicle-to-everything (V2X) communication, challenges persist in scalability, latency, revocation, and misbehavior detection. This work proposes a hybrid framework, with improvements including blockchain-based revocation, decentralized trust models, and privacy preservation using pseudonym rotation and zero-knowledge proofs, thus extending existing standards.
Internet of Underwater Things (IoUT) introduces critical security challenges, especially for protecting distributed infrastructures in resource-constrained environments. Conventional asymmetric and centralized authentication models are unsuitable due to computational and communication overhead, while symmetric approaches lack robustness without trusted storage or hardware. We propose a non-interactive, asynchronous authentication protocol based on NIZKP, combining PUFs-derived secrets with decentralized identifiers on a distributed ledger. This approach enables direct node authentication with cryptographically verifiable identity binding, minimal resource usage, offline verification, and full support for asynchronous operation in constrained environments. The protocol is formally analysed and implemented on COTS hardware without additional secure components. Evaluation shows low energy consumption (827.2 mJ), minimal communication overhead (113 B, 1.513s, 817.9 mJ), and reasonable execution times (worst case ≈ 5.310s), outperforming state-of-the-art solutions in the first four metrics.
Mohammed Ziaul Haider, Tayyaba Noreen, Mishah Uzziél Salman, Marcos Dias de Assunção · 5 authors
Cross-chain bridges and oracle DAOs represent some of the most vulnerable components of decentralized systems, with more than 2.8 billion lost due to trust failures, opaque validation behavior, and weak incentives. Current oracle designs are based on multisigs, optimistic assumptions, or centralized aggregation, exposing them to attacks and delays. Moreover, predictable committee selection enables manipulation, which threatens data integrity across chains. We propose V-ZOR, a verifiable oracle relay that integrates zero-knowledge proofs, quantum-grade randomness, and cross-chain restaking to mitigate these risks. Each oracle packet includes a Halo 2 proof verifying that the reported data was correctly aggregated using a deterministic median. To prevent committee manipulation, VZOR reseeds its VRF using auditable quantum entropy, ensuring unpredictable and secure selection of reporters. Reporters stake once on a shared restaking hub; any connected chain can submit a fraud proof to trigger slashing, removing the need for multisigs or optimistic assumptions. A prototype in Sepolia and Scroll achieves sub-300k gas verification, one-block latency, and a $\mathbf{1 0} \times$ increase in collusion cost. V-ZOR demonstrates that combining ZK attestation with quantum-randomized restaking enables a trust-minimized, high-performance oracle layer for cross-chain DeFi.
With the rapid development of the Internet of Things (IoT), the security and privacy of personal data has received widespread attention. Federated learning models protect personal privacy data through distributed collaborative training models, but it has been shown that personal privacy data can be inferred from uploaded parameters. Federated learning models also face the challenges of privacy leakage risk, computational inefficiency and lack of verifiability. Existing differential privacybased federated learning models and homomorphic encryptionbased federated learning models are unable to balance model accuracy and security. They also face the problem of inefficient computation of client-side local data and high communication overhead. Therefore, in this paper, we propose a federated learning framework (RGC-FL) based on Re-randomizable Garbled Circuits (RGC), which achieves a balance between privacy protection and computational efficiency through dynamic encryption and re-randomization techniques. The model updates are first encrypted at the client using the obfuscated circuits and then uploaded to the server, and then the ciphertext updates are aggregated by the re-randomization technique to avoid the leakage of the original data. Secondly, the client verifies the correctness of the server’s aggregation results by zero-knowledge proof. Finally based on DDH assumption and Kilian randomization technique to defend against hybrid attacks in dynamic input scenarios. We experimentally show that the model accuracy of RGC-FL on MNIST and CIFAR-10 datasets is 97.3% and 83.9%, respectively, which is close to plaintext federated learning and significantly outperforms the Differential Privacy (DP-FL) and Fully Homomorphic Encryption scheme (FHE-FL). In terms of efficiency, the training time for a single round is only 32% of that of FHE-FL (12.4 sec vs. 38.7 sec), and the communication overhead is reduced by $80 \%(5.2 \mathrm{MB}$ vs. 25.6 MB). This paper provides an efficient and secure solution for federated learning in highly privacy-sensitive domains and promotes the wide application of AI under compliance requirements.
Traceable ring signatures (TRSs) allow a signer to create a signature that maintains anonymity while enabling traceability if needed. It merges the characteristics of traditional ring signatures with the ability to trace signers, making it ideal for applications that demand both confidentiality and accountability. In a TRS scheme, a ring of potential signers generates a signature on a message without disclosing the actual signer’s identity. However, the identity can be traced if the signer uses the same tag for multiple signatures. This paper introduces a novel formal construction of TRS under universally composable (UC) security. We integrate verifiable random functions (VRFs) and zero-knowledge proofs for membership, employing Pedersen commitments. Our signature schemes maintain a logarithmic size while preserving the UC security guarantees. Additionally, we explore the potential to extend the property of one-time anonymity in TRS to K-time anonymity.
Mahendran Chinnaiah, A. Kumar Chandra Gupta, Saurabh Srivastava, Ashok Ghimire
At a time when data privacy laws and cyber-attacks are on the rise, Zero-Knowledge Proofs (ZKPs) and Artificial Intelligence (AI) hold the potential of a transformational paradigm of safe (privacy-preserving) machine learning (ML) inferences. In this paper, we present a new architecture that facilitates Zero-Knowledge AI, in which sensitive data inputs and internal model parameters remain unknown during the model inference procedure across distributed ecosystems. The proposed framework can help preserve privacy standards like GDPR and HIPAA, inference accuracies, and scalability of these inferences by utilising mechanisms to observe cryptographic zero-knowledge protocols, as well as federated learning protocols. We describe the construction of ZK-friendly models to apply to neural inference pipelines, efficient zk-SNARK-based model validation, decentralized trusting schemes, and privacy-respecting model auditing. Testing over a variety of healthcare and financial datasets indicates that our Zero-Knowledge AI solution results in high privacy guarantees with limited throughput losses. The work provides a strong basis on how to implement trusted and privacy-first AI systems in the real life and distributed operating environment.
R. Uma Mageswari, K. Nallarasu, L. Remegius Praveen Sahayaraj, A. A. Abd El-Aziz
Blockchain-driven Federated Learning (BFL) represents an intriguing intersection of two cutting-edge technologies: blockchain and federated learning. A form of distributed machine learning technique known as Federated Learning (FL) aims to preserve the privacy of user data. FL supports privacy preservation, decentralization, and collaborative learning by the means of retaining user data on local devices, training the models without sharing raw data, minimizing the danger of leakage of user data, and avoiding the need for centralized data storage. Beyond these attractive features held by FL, arduous challenges like ensuring secure model aggregation and communication, failure of single points, vulnerability faced by centralized parameter servers, minimal client participation due to lack of motivation, and incentives lacking are encountered. To provide a solution for these obstructions, an innovative idea is to integrate FL with blockchain, which is another decentralized cutting-edge technology. This collaboration leads to a much more robust BFL. FL can be enhanced through blockchain via data provenance where blockchain records data origins as well as model updates by using consensus mechanisms. The consensus mechanisms here ensure the decentralized model integrity, and then the Smart Contracts ensure the automated reward distribution to incentivize participation. FL and blockchain technology use cases are mostly involved in sectors like healthcare, finance, transportation, smart cities, etc. independently. These two core technologies, FL and blockchain, are constructively combined to achieve inviolable higher-end applications, which promise minimized data leakage risk in collaborative data sharing.
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.
Ryota Takenouchi, Haruki Kurisaka, Yue Su, Kien Nguyen · 5 authors
As quantum computing threatens the security of traditional cryptographic algorithms used in blockchain systems, integrating post-quantum cryptography (PQC) into IoT-blockchain environments has become a critical research direction. However, the practicality of PQC in resource-constrained IoT devices remains largely unexplored. This paper presents a fully operational IoT-blockchain system that concurrently runs PQC algorithms and blockchain processes on Raspberry Pi 4 nodes using Ethereum and Proof-of-Stake consensus. We implement and evaluate three NIST-recommended PQC algorithms (i.e., Dilithium, FALCON, and SPHINCS+) and assess their impact on CPU utilization, memory and disk usage, and power consumption. The results show that SPHINCS+ introduces significant overhead due to its hash-based signature scheme, whereas Dilithium and FALCON have minimal performance impact and are more suitable for constrained environments. All PQC algorithms, however, lead to a notable increase in disk usage. This study provides the first system-level evaluation of PQC-integrated IoT-blockchain platforms and offers practical insights for secure and efficient deployment in the post-quantum era.
M Ramya, B S Anuvarshini, G Kanika, A Lohith · 5 authors
The identity verification is required by many digital platforms, it raises real privacy and security risks when users are required to submit identity documents multiple times. When identity documents are stored in centralized databases data breaches or misuse from within the organization holding the data may occur. To mitigate risks associated with centralized data storage models, a decentralized identity management system is being created using blockchain and distributed file storage system architecture. When users submit identity documents, the data is stored securely in a decentralized file storage system and a cryptographic hash is generated that will be used to reference the data at a later time. This hash will then be recorded on a blockchain that is immutable and creates a secure log. Identification is verified through a manual verification process by authorized reviewers who approve or reject submissions all while the user is able to monitor submission status. User identity privacy is maintained by using zero-knowledge proofs to verify distinct attributes of identity that are required without revealing the identity data itself. With proof verification attaining a success rate of $98.6 \%$, admin validation accuracy of $96.4 \%$, and gas optimization leading to a $\mathbf{3 1 \%}$ decrease in execution costs in comparison to baseline smart contract calls, the system showed outstanding operational efficiency. This will provide users with more control over their identity documents while also providing secure, transparent and privacy preserving identity verification to be used in modern digital services.
Abstract Verifiable encryption enables the decryption to be taken on properly generated ciphertexts, by making the encryptor provide a zero-knowledge proof. To meet the quantum-safe application requirements, such as key escrow, Lyubashevsky et al. proposed a one-shot verifiable encryption (LN17 scheme) based on the hardness of lattice problems. In their scheme, the FSwA-type zero-knowledge proof was obtained using rejection sampling on a discrete Gaussian distribution. In this paper, we present a construction of verifiable encryption that utilizes rejection sampling on bimodal Gaussian to get the associated zero-knowledge proof. Our new construction, while exhibiting a weaker soundness property than LN17 scheme, benefits from a smaller proof size, leading to a reduced size of the verifiable ciphertext. As for the weaker soundness property, it supports some applications such as key escrow where honestly generated verifiable ciphertexts are more useful to be decrypted out in the hope of doing some further computation tasks. We provide the efficiency comparison of the new construction by instantiating it with several sets of concrete parameters.
Stephen Kirkman, Richard E. Newman, Christopher Garcia
The Ethereum Trilemma states that security, scalability, and decentralization cannot all be achieved at the same time in a blockchain. We call this the ‘Ethereum’ Trilemma because there are solutions to generic blockchain scalability. Due to the added computation, Ethereum has certain challenges that so far cannot be solved without going to Layer 2 or completely changing the base chain. Layer 2 scaling techniques are add-ons to Layer 1 (the base chain). These scaling techniques, may, in some cases reduce the security of the blockchain - but it depends on the definition of security one uses. We examine the implications of Layer 2 scaling (particularly optimistic rollups since they are most popular) and note that decentralized computation and smart contract security have been relegated to a back seat at best or tossed out completely at worst - validation appears to be left behind. This is a gap in rollups that neither Optimistic Rollups nor ZK-Rollups fill. We propose to fill that gap with what we call an $\mathrm{n} \%$-Validations Rollup that bring some validation back - not fully decentralized, but not completely centralized either; the best of both worlds. In our vision, the user needs more options to specify their desired level of decentralized validation. Currently, if you use a rollup, you have potentially no Layer 1 validation. On the other extreme, too much validation is the scaling roadblock. This position paper suggests avenues for research of these issues. This is a transitional period for Ethereum and Layer 2 appears to have become the wild west.
Zero-knowledge Proof (ZKP), is an effective cryptographic primitive that allows one party to verify the correctness of a given statement without disclosing any additional information. It plays a central role in applications such as blockchain transactions and cryptocurrencies. However, implementations of ZKP suffer from the most time-consuming task called Multi-Scalar Multiplication (MSM). Existing works and evaluation criteria primarily emphasize speed enhancement, but overlook optimizations of area overhead. In this paper, a FPGA-based accelerator FusionMSM is designed to reduce the overall latency but also improve area overhead. We attribute the bottleneck of MSM to a three-layer pyramid, including the finite field arithmetic, point operations on elliptic curves and scheduling. For modular arithmetic, we propose an efficient and non-Montgomery modular multiplier by utilizing hybrid multiplication strategy and optimizing multi-bit LUT-based modular reduction. It obtains 1.11 x less area cost and 2.00 x speed-up versus the modular multipliers used in ZKP acceleration works. For point operations, we design a unified and fully pipelined point addition unit, which can run at 500 MHz, the highest frequency in the reported works. On top of that, we present a greedy mechanism to resolve potential collisions, which can reduce the idle cycles of the point addition unit and improve its utilization. As far as we know, FusionMSM achieves the best performance compared to other FPGA-based and ASIC-based works for the input sizes from 218 to 226. For the degree of 220, FusionMSM only needs 12.4% of time in Hardcaml, 24.54% of time in PipeMSM on FPGA, and 36.41% of time in ASIC-based work PipeZK. It also utilizes less resources, resulting in a 90.93% reduction in URAMs, 35.24% reduction in FFs and 47.59% reduction in CARRY8s. Compared to GPU-based implementations, FusionMSM delivers comparable performance but with a lower power of 24.5 W.
L. Domven, Aliyu Danladi Hina, A. M. Kwami, C. M. Miri · 5 authors
This study proposes a secure mobile voting system that integrates elliptic curve cryptography (ECC) with secure multiparty computation (SMPC) to guarantee vote confidentiality, integrity, and verifiability. Designed to enable scalable, privacy-preserving elections via mobile devices, the system authenticates voters using registered numbers and records ballots as encrypted points on an elliptic curve. Encrypted votes are published on a public bulletin board alongside zero-knowledge proofs to ensure their validity. To safeguard decryption, Shamir’s secret sharing distributes keys among trusted authorities, enabling collective tallying without exposing individual votes. The system incorporates ECC-based secret sharing, homomorphic encryption, and zero-knowledge proofs, leveraging the hardness of the elliptic curve discrete logarithm problem (ECDLP) for robust security. Both experimental and theoretical evaluations demonstrate that ECC significantly improves computational efficiency and scalability, making the system well-suited for resource-constrained environments. Overall, the integration of ECC and SMPC offers a practical, efficient, and secure framework for mobile elections, effectively balancing privacy, security, and performance.
Thibauld Feneuil, Matthieu Rivain, Auguste Warmé-Janville
Side-channel attacks pose significant threats to cryptographic implementations, which require the inclusion of countermeasures to mitigate these attacks. In this work, we study the masking of state-of-the-art post-quantum signatures based on the MPC-in-the-head paradigm. More precisely, we focus on the recent threshold-computation-in-the-head (TCitH) framework that applies to some NIST candidates of the post-quantum standardization process. We first provide an analysis of side-channel attack paths in the signature algorithms based on the TCitH framework. We then explain how to apply standard masking to achieve a d-probing secure implementation of such schemes, with performance scaling in O(d2), for d the masking order.Our main contribution is to introduce different ways to tweak those signature schemes towards their masking friendliness. While the TCitH framework comes in two variants, the GGM variant and the Merkle tree variant, we introduce a specific tweak for each of these variants. These tweaks allow us to achieve complexities of O(d) and O(d log d) at the cost of non-constant signature size, caused by the inclusion of additional seeds in the signature. We also propose a third tweak that takes advantage of the threshold secret sharing used in TCitH. With the right choice of parameters, we show how, by design, some parts of the TCitH algorithms satisfy probing security without additional countermeasures. While this approach can substantially reduce the cost of masking in some part of the signature algorithm, it degrades the soundness of the core zero-knowledge proof, hence slightly increasing the size of the signature.We analyze the complexity of the masked implementations of our tweaked TCitH signatures and provide benchmarks on a RISC-V platform with built-in hash accelerator. We use a modular benchmarking approach, allowing to estimate the performance of diverse signature instances with different tweaks and parameters. Our results illustrate how the different variants scale for an increasing masking order. For instance, for a masking order d = 3, we obtain signatures of around 14 kB that run in 0.67 second on a the target RISC-V CPU with a 250MHz frequency. This is to be compared with the 4.7 seconds required by the original signature scheme masked at the same order on the same platform. For a masking order d = 7, we obtain a signature of 17.5 kB running in 1.75 second, to be compared with 16 seconds for the stardard masked signature.Finally, we discuss the extension of our techniques to signature schemes based on the VOLE-in-the-Head framework, which shares similarities with the GGM variant of TCitH. One key takeaway of our work is that the Merkle tree variant of TCitH is inherently more amenable to efficient masking than frameworks based on GGM trees, such as TCitH-GGM or VOLE-in-the-Head.