Electronic voting is crucial for contemporary democratic processes. However, conventional systems often struggle with a single point of failure and insufficient support for intricate voting semantics, especially in ranked-choice elections requiring distinct ranking constraints. Although blockchain technology enhances fault tolerance and auditability, existing blockchainbased solutions primarily focus on simpler voting schemes, encountering difficulties in effectively verifying privacy-preserving ranked ballots. To address this gap, we propose AranVoting, an innovative blockchain-based ranked-choice voting scheme designed to ensure anonymity and fairness through homomorphic encryption. AranVoting employs a structured matrix ballot format alongside zero-knowledge proof to ensure the correctness of the ballot format. Furthermore, we introduce a smart contractdriven counting mechanism that facilitates the availability and transparency of ballot tallying through gradient incentive and committee election algorithms. Our security and performance evaluations demonstrate that AranVoting provides secure rankedchoice voting, effectively guaranteeing essential security properties such as anonymity, correctness, and verifiability while maintaining practicality and reasonable computational overhead throughout the voting process.
Code-based group signatures are a promising candidate for post-quantum cryptography, but existing code-based group signature schemes struggle with the challenges of large signature sizes caused by zero-knowledge proofs. To address this issue, we propose a novel and practical code-based group signature scheme built upon the Schnorr-Lyubashevsky paradigm. Our construction achieves constant-size signatures and public keys, independent of the group cardinality, and its security is formally proven in the random oracle model under the hardness assumptions of the Syndrome Decoding (SD) and Decoding One Out of Many (DOOM) problems. To alleviate the performance bottleneck of rejection sampling, we design and implement a batch processing optimization for the signing algorithm, which significantly accelerates signature generation by applying vectorization to the most computationally intensive operations. Experimental results show that the optimization renders signing practical. Our scheme features the most compact signature size among existing codebased group signature schemes. All related code is open-sourced and available at https://github.com/Latters/CodeBasedGroupSig/.
A zero-knowledge proof demonstrates that a fact (like that a Sudoku puzzle has a solution) is true while, counterintuitively, revealing nothing else (like what the solution actually is). This remarkable guarantee is extremely useful in cryptographic applications, but it comes at a cost. A classical impossibility result by Goldreich and Oren [J. Cryptol. ‘94] shows that zeroknowledge proofs must necessarily sacrifice basic properties of traditional mathematical proofs - namely perfect soundness (that no proof of a false statement exists) and non-interactivity (that a proof can be transmitted in a single message). Contrary to this impossibility, we show that zero-knowledge with perfect soundness and no interaction is effectively possible. We do so by defining and constructing a powerful new relaxation of zero-knowledge. Intuitively, while the classical zero-knowledge definition requires that an object called a simulator actually exists, our new definition only requires that one cannot rule out that a simulator exists (in a particular logical sense). Using this, we show that every falsifiable security property of (classical) zero-knowledge can be achieved with no interaction, no setup, and perfect soundness. This enables us to remove interaction and setup from (classical) zero-knowledge in essentially all of its applications in the literature, at the relatively mild cost that such applications now have security that is “game-based” instead of “simulation-based.” Our construction builds on the work of Kuykendall and Zhandry [TCC ‘20] and relies on two central, longstanding, and well-studied assumptions that we show are also necessary. The first is the existence of non-interactive witness indistinguishable proofs, which follows from standard assumptions in cryptography. The second is Krajíček and Pudlák’s 1989 conjecture that no optimal proof system exists. This is one of the main conjectures in the field of proof complexity and is the natural finitistic analogue of the impossibility of Hilbert’s second problem (and, hence, also Gödel’s incompleteness theorem). Our highlevel idea is to use these assumptions to construct a prover and verifier where no simulator exists, but the non-existence of a simulator is independent (in the logical sense of unprovability) of an arbitrarily strong logical system. One such logical system is the standard axioms of mathematics: ZFC.
As vehicular ad hoc networks (VANETs) increase in size and complexity, ensuring secure, flexible, and privacy-preserving vehicle-to-infrastructure (V2I) authentication remains a major challenge. Existing protocols often focus solely on identity verification, overlooking the need for access control based on vehicle attributes. Furthermore, vehicles must obtain authentication credentials from various trusted entities, including automakers, regulators, and government agencies. However, the absence of a unified credential issuance mechanism introduces fragmentation and inconsistencies during the registration process. To address these issues, we propose a V2I authentication protocol, called PriV2I, that integrates distributed credential issuance, attribute-based access control, and strong anonymity guarantees. During vehicle registration, our approach uses Shamir’s Secret Sharing with a threshold t of n across multiple certification authorities (CAs) to consolidate credentials. A vehicle credential can only be issued by a predefined threshold number of CAs, enhancing security and flexibility. Within the authentication protocol, Pointcheval-Sanders (PS) signatures enable fine-grained access control based on vehicle attributes such as type and role. Meanwhile, noninteractive zero-knowledge proofs protect identity privacy by allowing vehicles to prove credential possession and policy compliance without revealing sensitive information. The proposed scheme also supports batch authentication at Roadside Units (RSUs) to efficiently handle high-density environments and includes a comprehensive revocation mechanism to trace and revoke malicious vehicles promptly and securely. In our implementation, the computation cost during the authentication phase is 75.58 ms. The communication overhead per authentication exchange is 992 bytes across two messages. Overall, the protocol provides a secure, scalable, and privacy-preserving solution tailored to modern VANET environments.
Even in this day and age, when digital technologies are becoming more and more prevalent, it is still extremely important for democratic systems to maintain the honesty and openness of their voting procedures. This article introduces NextGenVote, a decentralised online voting platform developed to address the security, transparency, and confidence issues traditional electronic voting systems face. Automation of election operations, including voter registration, candidate administration, ballot casting, and result computation, is achieved through smart contracts written in the Solidity programming language. The system is built on the Ethereum blockchain. MetaMask is a React-based frontend that uses Web3.js to connect to the blockchain. MetaMask is responsible for ensuring that user authentication and transaction signatures are secure. Therefore, to prevent unauthorised manipulation, the platform utilises a role-based access control approach that clearly distinguishes between administrative capabilities and voter credentials. NextGenVote assures that election results are tamper-proof, traceable, and auditable. It was deployed and tested in a local blockchain environment powered by Ganache. The system provides a solid foundation for scalable, secure, and transparent digital elections by eliminating centralised intermediaries and relying solely on processes executed on the blockchain.
Abstract Zero knowledge succinct non-interactive arguments of knowledge protocol (zk-SNARK) is an application oriented variant of zero knowledge proof, which enables a prover to convince a verifier that a statement is true, without revealing any other information beyond the correctness of the statement itself. Due to its powerful capabilities and high efficiency, it has been widely deployed in various blockchain based applications to provide privacy and scalability. While these applications place high demands on small proof size, fast verification and decentralization, currently available zk-SNARK with the shortest proof size and the fastest verification speed is in the common reference string (CRS) model, that is they require the trusted setup. After the pioneering results proposed by Bellare et al. in ASIACRYPT 2016, there have been lots of efforts to construct zk-SNARKs that satisfy subversion zero knowledge (S-ZK) and standard soundness from the zk-SNARK in the CRS model. These constructions could be regarded secure in the bare public key (BPK) model because that the equivalence between S-ZK in the CRS model, and uniform non-black-box zero knowledge in the BPK model has been proved by Abdolmaleki et al. in PKC 2020. Thus, compared to the CRS model, the BPK model better characterizes decentralized blockchain based application such as cryptocurrencies and anonymous credentials. In this study, by leveraging the power of random oracle (RO) model, we proposed the first publicly verifiable non-uniform ZK zk-SNARK scheme in the BPK model maintaining comparable efficiency with its conventional counterpart, which can also be compatible with the well-known transformation proposed by Bitansky et al. in TCC 2013 to obtain an efficient designated-verifier zk-SNARK. We achieve this goal by only adding a constant number of elements into the CRS, and using an unconventional but natural method to transform Groth’s zk-SNARK in EUROCRYPT 2016. In addition, we propose a new speed-up technique that provides a trade-off. Specifically, if a logarithmic number of elements are added into the CRS, according to different circuits, the CRS verification time in our construction could be approximately 9–23% shorter than that in the conventional counterpart.
Federated learning (FL), which allows collaborative machine learning without requiring the centralisation of sensitive data, has become a game-changing concept for private and safe data analysis in decentralised networks. FL enables edge devices or local nodes, such as smartphones, IoT devices, or healthcare facilities, to learn shared models remotely and send only model changes to a central server, in contrast to traditional methods that call for raw data aggregation. This framework lowers communication overhead, mitigates regulatory problems, and greatly improves data privacy and security. FL provides a workable and scalable way to create superior machine learning models in decentralised networks, where data is naturally dispersed and frequently subject to stringent privacy laws. Nevertheless, there are still issues to be resolved, such as managing non-IID data, making sure that systems are resilient to hostile attacks, and preserving effective communication. To further improve FL's privacy-preserving capabilities, recent developments like homomorphic encryption, safe multiparty computation, and differential privacy are being incorporated. This study examines the fundamentals of federated learning, goes over important methods for improving its security and privacy, and talks about how it may be used in a variety of industries, including as healthcare, finance, and smart cities. FL is one of the most important steps to safe and ethical AI in decentralized environments because it allows the collaboration of intelligence and preserves data ownership.
The increasing use of decentralized finance (DeFi) accelerates the demand for trustless, secure mechanisms for crosschain token exchange. This paper outlines a complete model for atomic token swaps based on the Hashed Timelock Contract (HTLC) scheme, allowing for intermediary-free token exchanges across disparate blockchain systems. The system makes use of the local blockchain simulation framework, Ganache, to design and test cross-chain interactions in a sandbox environment. To improve the decision-making capabilities for users, a real-time cryptocurrency price forecasting subsystem is added which utilizes machine learning models to analyze and predict the market and its volatility. Additionally, the system harnesses Generative AI capabilities through prompt engineering to tailor investment advice for individual users by analyzing the market, their preferred risk level, expected returns, and provide investment strategies aligned with users' preferences. Apart from sophisticated trading algorithms, the solution also offers a simple dashboard for market price monitoring and performs rapid token swaps at the user's command. Smart contracts are implemented using Solidity, token and price feeds are ports to Web3.js, predictive analytics is done in Python, while the frontend and backend are structured in Next.js alongside Node.js. System testing validates hypotheses on the provision of secure cross-chain swaps within one transaction without compromising.
The widespread deployment of Internet of Things (IoT) devices has driven their segmentation into distinct trust domains for the purpose of governance, creating a critical need for secure cross-domain authentication (CDA). CDA must preserve both anonymity and traceability of device identities to enable trustworthy data exchange. However, existing approaches, while exploring this trade-off, remain vulnerable to single points of failure and Sybil attacks—threats that are especially severe for unattended and resource-constrained devices. In this paper, we propose a Self-Sovereign and Supervised Cross-domain authentication scheme (SCross) to tackle these issues. The main building block we designed is a pseudonym management scheme (PMS) that allows devices to generate and use pseudonyms without relying on a trusted party. Although devices has full control of their identities, PMS still ensures traceability, Sybil resistance, and revocability. We define the formal security models of PMS, instantiate it under two different approaches, namely group signature (SCross-GS) and zero-knowledge succinct non-interactive arguments of knowledge (zkSNARKs, SCross-ZK), and present security proofs for our proposal. We implemented and evaluated SCross. The result shows that our scheme achieves an effective trade-off between security and efficiency.
In the field of artificial intelligence, secure model inference is essential for protecting data confidentiality, which allows users to interact with trained models for decision-making support without privacy leakage. However, current secure inference methods often overlook the simultaneous verification of data origins for both user inputs and model weights, which is crucial for maintaining the integrity of inference outcomes. In this study, we present a novel verifiable secure inference scheme that leverages blockchain to enhance the verifiability of both the inference process and the origins of user inputs and model weights. We integrate the decentralized ledger to store the committed inputs and weights, serving as convincing data origins. We then transform neural networks into zero-knowledge proof constraints with optimized structures for the inference process. To illustrate its application scenario, we propose a regulatable privacy-preserving transaction scheme. Its regulation depends on anomaly detection on private transactions without privacy leakage, which takes the encrypted ledger as the data source and the committed detection model as the parameter source to perform our verifiable secure inference. We provide rigorous security proofs for our schemes, demonstrating their authenticity and privacy. We implement them to demonstrate their scalability through analyzing their computational and communication performance.
Zero-knowledge proofs (ZKPs) are central to secure and privacy-preserving computation, with zk-SNARKs and zk-STARKs emerging as leading frameworks offering distinct trade-offs in efficiency, scalability, and trust assumptions. While their theoretical foundations are well studied, practical performance under real-world conditions remains less understood. In this work, we present a systematic, implementation-level comparison of zk-SNARKs (Groth16) and zk-STARKs using publicly available reference implementations on a consumer-grade ARM platform. Our empirical evaluation covers proof generation time, verification latency, proof size, and CPU profiling. Results show that zk-SNARKs generate proofs 68x faster with 123x smaller proof size, but verify slower and require trusted setup, whereas zk-STARKs, despite larger proofs and slower generation, verify faster and remain transparent and post-quantum secure. Profiling further identifies distinct computational bottlenecks across the two systems, underscoring how execution models and implementation details significantly affect real-world performance. These findings provide actionable insights for developers, protocol designers, and researchers in selecting and optimizing proof systems for applications such as privacy-preserving transactions, verifiable computation, and scalable rollups.
Cloud Computing (CC) is an excellent platform that is widely used to share information as well as services between various departments, customers, and other parties. However, during the transmission of sensitive data, effective security management is essential to ensure data privacy by avoiding unauthorised access. To address this, an efficient key agreement protocol, which is a combination of Elliptic Curve Cryptography with Zero Knowledge Proof (ECC-ZKP) model, is proposed to enhance security management in a cloud environment. The ECC-based encryption model provides equivalent security with much smaller key sizes compared to traditional cryptography approaches. Moreover, this lightweight model reduces computational load and speeds up operations in large-scale cloud environments. The integration of the ZKP model allows users to prove their identity and get access without disclosing any sensitive credentials, which leads to an increase the data confidentiality. Initially, the login and registration of users in the cloud is done by submitting all their details to the service provider. Then, a hash value and bound values are computed, and a secret key is generated by the service provider, which is sent to the client for accessing the data from the cloud. The experiment results of the proposed ECC-ZKP model achieved Makespan of 4.8 ms for 100 tasks, which is better than traditional key agreement models, such as ECC-based authenticated key agreement, respectively.
Basil Hanafi, Mohammad Ubaidullah Bokhari, Mudasir Ahmad Wani, Kashish Ara Shakil · 5 authors
Various types of research are being carried out to advance in the field of cryptography and develop a more robust technique for security. Adversarial neural cryptography (ANC) is a recent development in this extension, which possesses huge potential to be implemented in various domains. There is a continuous need for the development of more adaptive techniques to secure data while in communication using deep learning and other applied artificial intelligence techniques, which serves as the motivation for this work stems from the increasing need for adaptive, robust encryption mechanisms to address the limitations of traditional cryptographic techniques in securing sensitive blockchain transactions. This article proposes a new approach for the protection of private smart contracts on blockchain systems via ANC. The proposed method in the research is dynamic adversarial training using three neural networks to secure smart contract transactions. It optimizes the encryption and decryption processes against evolving cyber threats. The algorithm strives to attain a high key agreement rate (KAR) and a lower Eve’s decryption failure rate (EDFR) eventually proving its efficacy in attaining privacy and security in blockchain applications and adaptability. This research will incite more studies on ANC and its practical implementations in ensuring private smart contracts and overcoming the present cryptographical approaches with significant development because of their potential.
Abstract In today’s world, most financial and personal transactions happen online. This makes data security a big concern. To address risks like data breaches, hacking, and identity theft, our project “Blockchain Secure Transaction” aims to create a dependable and decentralized system for secure digital payments. The system uses blockchain technology to ensure transparency and immutability in each transaction, eliminating the need for a central authority. The process starts with user registration, where details are securely stored along with a picture password for better recognition. During login, users must pass both the picture password and a biometric check. This ensures that only the account. Once verified, the user enters the dashboard, where transactions begin through Zero-Knowledge Proof (ZKP) for privacy-preserving verification. Every transaction is validated with smart contracts. If there’s any mismatch or automatically blocks or freezes the transaction. The backend uses Java, while Firebase stores user data securely, and 2 factor.in enables OTP-based authentication. The frontend interface, designed in React (app.jsx), allows smooth navigation across pages. By combining blockchain, smart contracts, biometric authentication, and ZKP, this project provides a secure and user-friendly platform that prevents unauthorized access and builds user trust in digital payment systems Keywords Blockchain, Secure Transaction, Zero Knowledge Proof (ZKP),Smart Contract, Biometric Authentication, Picture Password, Decentralized System, Data Privacy, Transaction Verification, Firebase Integration, 2 factor.in OTP Authentication.
Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive that enables a prover to convince a verifier that something is true without leaking the private witness. Current zkSNARKs face significant computational costs in generating proofs, which restricts their use in areas like private payments, confidential smart contracts, and anonymous credentials. Private delegation offers a practical solution by outsourcing the heavy computation to powerful external workers without leaking any private information. In this work, we propose HyperSiniel, an efficient private delegation framework for general zkSNARKs that achieves a new feature called guaranteed output delivery (GOD). HyperSiniel is designed to be compatible with any universal zkSNARKs constructed from a polynomial interactive oracle proof (PIOP) and a polynomial commitment scheme (PCS). It enables a computationally limited delegator to outsource proof generation to several workers in a fully non-interactive and privacy-preserving manner. Compared to the most state-of-the-art frameworks (e.g., Siniel [NDSS'25]), HyperSiniel ensures that the delegator always receives a correct proof, regardless of malicious worker behavior. We implement HyperSiniel and compare the performance with Siniel across varying bandwidths and circuit sizes. Under low-bandwidth conditions (10MBps), HyperSiniel incurs only an additional 25% overhead compared with Siniel, while the total running time of HyperSiniel is almost identical to Siniel under high-bandwidth settings (1000MBps). These results show that the strong robustness guarantee of GOD in HyperSiniel comes almost for free, making it a practical and secure solution for real-world zkSNARK delegation.
Open access
Cryptography and Data Security
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
While decentralized authentication mechanisms have gained significant attention for enabling user-centric identity management without centralized authorities, the critical counterpart - authenticated key exchange (AKE) in decentralized settings - remains understudied. Although it forms the basis for secure communication in decentralized scenarios, shifting existing AKE protocols to decentralized settings is impractical: the trust assumption is different, and the insufficient support for dynamic identity attributes, etc. To address these challenges, we present a novel decentralized AKE protocol that innovatively integrates attribute authentication with key exchange through multi-party secure computation. Building upon MPCAuth's foundational framework (S&P 23), our protocol goes further to provide key exchange based on authentication of real-world attributes such as a digital passport and email address, etc. Our protocol establishes a new paradigm for decentralized AKE without complex credential operations and heavy zero-knowledge proof. The core of our protocol is a distributed way to securely reconstruct the attributes and establish a session key. We further evaluate its performance across multiple servers. Experimental results on servers under 5 demonstrate that it can finish the full AKE procedure in an acceptable time, enabling efficient and scalable multi-party key AKE in distributed environments.
Anurag Anand Duvey, Chandrashekhar Goswami, Amit Kumar Goel
Federated Learning (FL) gives opportunity to decentralized model training without the raw data's revealing. But in actual real-world implementation faces certain number of challenges. These include trust in client updates, verifiable end-to-end privacy promises, equitable contributor compensation, and accountable aggregation. This paper provides a design-first architecture that addresses these issues by integrating concise zero-knowledge proofs (zk-SNARKs) for trustless verification with smart-contract arrangements. This approach is for verification, secure aggregate pooling, and reward settlement. Our design is consisting a structure of five-layer stack, named as Client, Proof, Blockchain, Incentive, and Governance. It highlights end-to-end workflows for the generation of proof for updates, verifying them on chain, anchor-off chain aggregation sequence anchoring, and allocate contribution-matching token payouts. We specify clearly smart-contract interfaces called as aggregation, registry, incentivization, zk-circuit objectives, and several scaling controls like proofs/aggregated in batch or Layer-2/rollup rollouts and the anchor-Merkel. The design also implements Shapley-like contribution measures and ERC-compact reward settlement. It incorporates Sybil-resistance and vesting primitives to lean against game. We define a crisp threat model, discuss security and privacy trade-offs, and suggest evaluation using healthcare and IoT benchmarks. These assess the learning utility, resilience to poisoning, fairness of the payouts, and system costs in terms of gas fee. Moreover, by training and incentivization by smart contracts at design level, we create a foundation for future prototyping and rigorous empiric testing. This sets a realistic path from the design framework to effective workable, end-to-end Privacy-preserving Federated Learning deployment.
Federated Learning (FL) enables multiple participants to collaboratively train a shared model without sharing their private data. However, FL remains vulnerable to malicious clients submitting incorrect updates to disrupt training. To address this, we formalize each client’s local training step as a Nondeterministic Polynomial-time (NP) statement, verifiable via zero-knowledge proofs (ZKPs) at every round. We propose Jolt-FL, the first general-purpose verifiable FL framework that immediately detects and excludes malicious clients upon their first dishonest action – without relying on heuristics, statistical assumptions, or multi-round analysis. Built on Jolt’s zkVM, a state-of-the-art zero-knowledge virtual machine (zkVM) developed by a16zcrypto, Jolt-FL guarantees training integrity and data privacy without trusted hardware or third-party intermediaries. By witnessing every computation step, it defends against a wide range of attack vectors, securely filtering dishonest updates even if up to 50% of clients are malicious, while preserving convergence and final model performance. To demonstrate feasibility, we implement a prototype featuring a complete end-to-end Convolutional Neural Network (CNN) for image classification using the MNIST dataset. To our knowledge, this is the first fully verifiable end-to-end CNN training under ZKPs without any custom circuit design. Our solution achieves competitive proof generation times, compact proof sizes, and low verification costs–all while preserving model accuracy on par with standard FL.
Johnny Y. Solano Marinho, Eryk Schiller, Arthur Debauge, Noria Foukia
This paper presents a framework that integrates the Coalition for Content Provenance and Authenticity (C2PA) standard with Zero-Knowledge Proofs (ZKPs), specifically the Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs), to enable verifiable yet privacy-preserving authentication of digital images. Using the ZoKrates toolkit, the system derives a non-revealing fingerprint from the image, generates a succinct proof of integrity, and embeds this proof into C2PA-compliant metadata without exposing the underlying content. The proof can be verified locally or on the Ethereum blockchain using a Groth16 smart contract verifier, providing decentralized and auditably transparent validation. This capability allows journalists, victims, and legal professionals to attest to the existence and integrity of sensitive evidence while deferring its disclosure. Experimental results show that proof verification is highly efficient, requiring approximately 0.01 s, and that the entire workflow is reproducible within containerized environments. The proposed integration of zk-SNARKs with C2PA establishes a practical foundation for secure digital provenance, privacy-preserving evidence management, and strengthened trust in digital media ecosystems.
The digital transformation of education necessitates secure, private, and learner-centric methods for verifying academic credentials. Conventional verification processes expose sensitive personally identifiable information, creating privacy risks that conflict with data protection regulations like GDPR. Existing blockchain solutions for educational credential verification face persistent challenges including prohibitive transaction costs, privacy vulnerabilities, and inflexible verification models. This paper presents VeriZKP, a proof-of-concept architecture demonstrating gas-free credential verification on Ethereum using zero-knowledge proofs. The core innovation lies in separating on-chain trust anchoring from off-chain cryptographic computation, enabling a novel cost-elimination mechanism. The system leverages Ethereum’s view functions through pre-compiled verifier contracts to achieve zero gas consumption for verification operations while preserving privacy through selective disclosure mechanisms. Our prototype, evaluated on Ethereum Sepolia testnet, validates the fundamental feasibility of this approach. Results demonstrate complete elimination of verification costs, practical client-side proof generation times of 1.02-1.63 seconds on standard hardware, and support for multi-attribute credential verification. The architecture proves both economically viable and performant for blockchain-based identity systems.