Financial exclusion remains acute in Sub-Saharan Africa, where more than 350 million adults lack access to formal financial services. A defining barrier is the absence of verifable identity: in countries such as Mozambique, Tanzania, and Niger, over half of adults without mobile money accounts cite missing documentation as the primary obstacle. Existing remedies either centralise sensitive personal data, creating systemic privacy and security risks, or demand document-issuing infrastructure that does not yet exist in many communities. This paper proposes ZKP-IDFS (Zero-Knowledge Proof Identity for Financial Services), a decentralised, privacy preserving digital identity framework that lets individuals prove identity-related predicates to financial institutions without disclosing the underlying personal attributes. ZKP-IDFS combines Groth16 zk-SNARKs for succinct on-chain proof verification, Pedersen commitments for attribute hiding, a W3C-compliant Verifi-able Credential layer, and a lightweight USSD/SMS proof-relay channel designed for feature-phone users in low-connectivity environments. We formalise the cryptographic model, specify the system architecture, and present a simulated performance evaluation across four representative network conditions. Results from a controlled simulation study show that end-to-end proof generation and relay complete in under 4.2 seconds on entry-level Android handsets at 3G speeds, with on-chain verication costs below 0.003 USD on an EVM-compatible layer-2 chain; these results require validation in eld deployments. We further demonstrate compliance with FATF risk-based KYC guidance and with emerging African data-protection legislation.
Digital identity is critical, yet centralized providers create single points of failure—breaches have exposed billions of records—and quantum computing threatens the classical public-key cryptography (RSA/ECC) on which these systems rely. We present a system-level integration of blockchain, zero-knowledge proofs (ZKPs), and post-quantum cryptography (PQC) for privacy-preserving digital identity. A blockchain-based decentralized identifier (DID) system removes central databases; all signing and key-encapsulation operations use lattice-based PQC (CRYSTALS-Dilithium and Kyber); and selective disclosure is provided by Groth16 zk-SNARKs, with revocation via on-chain Merkle non-membership accumulators. We specify the full credential lifecycle—issuance, two-phase authentication, and revocation—with an explicit trust boundary separating the in-circuit Groth16 relation from the off-circuit issuer-signature check. We report a measured evaluation on a reference prototype: under liboqs 0.15.0, Dilithium-II signs/verifies in 0.19/0.06 ms and Kyber-512 encapsulates/decapsulates in 0.018/0.022 ms; a single-authentication Groth16 proof over the 21,715-constraint BN254 credential circuit takes <inline-formula> <tex-math notation="LaTeX">$\approx 981$ </tex-math></inline-formula> ms (snarkJS) and <inline-formula> <tex-math notation="LaTeX">$\approx 177$ </tex-math></inline-formula> ms (native rapidsnark) on byte-identical inputs, with <inline-formula> <tex-math notation="LaTeX">$\approx 40$ </tex-math></inline-formula> ms verification, a 723-byte proof, and <inline-formula> <tex-math notation="LaTeX">$\approx 243$ </tex-math></inline-formula>,000 gas for on-chain verification on a local EVM. A lifecycle harness with a passing revoked-credential negative test validates correctness. The signing and key-encapsulation layers are quantum-safe under current lattice assumptions; the Groth16 proof layer is classically secure only, and its post-quantum migration is identified as future work. End-to-end credential unforgeability is conditioned on an honest holder wallet performing the off-circuit signature check (Assumption 5). Every quantitative claim is labelled measured [M], simulated [S], assumption [A], or future work [F].
Current approaches to verifying AI training data compliance face a fundamental tension: copyright holders need to know whether their content was used in training (EU AI Act, Article 53(1)(d)), while model providers need to protect their training data as trade secrets (GDPR, trade secret law). Existing zero-knowledge proof systems for machine learning (ZKML) address this partially by providing proofs of non-membership for exact data points. However, real-world training pipelines involve tokenization, chunking, paraphrasing, and augmentation, rendering exact-match proofs insufficient. We identify a gap in the literature: no existing system combines semantic fingerprinting with zero-knowledge proofs to enable semantic non-membership verification. We propose an architecture for Zero-Knowledge Semantic Non-Membership (ZK-SNM) that enables a model provider to prove, without revealing any training data, that no document in their training corpus is semantically similar to a queried document above a specified threshold. We discuss the technical challenges, including the computational cost of similarity search within ZK circuits, and propose mitigation strategies based on locality-sensitive hashing and hierarchical verification. This position paper establishes the problem formulation and proposed architecture; experimental validation is left to subsequent work.
The growing frequency of malicious attacks on Internet of Things (IoT) devices has rendered conventional approaches with static label-dependent risk assessment models obsolete, especially when coping with unknown and continuo... | Find, read and cite all the research you need on Tech Science Press
GLYPH is a transparent verification layer for Ethereum for trustless on-chain verification of heterogeneous proof systems. It unifies upstream SNARK and STARK settlement through a single packed arity-8 sumcheck verifier over p = 2^128 - 159, while preserving upstream assumptions. The design centers on a universal adapter surface, UCIR compilation, and a chain-bound artifact interface for stateless verification. Benchmark evidence in the whitepaper reports 29.45k total transaction gas in recorded testnet receipts. This record includes the whitepaper and the formal proof appendix.
Ensuring transparency, security, and privacy in agricultural food supply chains is critical for maintaining consumer trust, regulatory compliance, and data integrity. Traditional centralized traceability systems suffer from several limitations, including data tampering risks, single-point failures, and potential privacy leakage. To address these challenges, this research proposes a privacy-preserving blockchain-based traceability framework that integrates the InterPlanetary File System (IPFS) with Zero-Knowledge Proofs (ZKPs). The framework leverages the Ethereum blockchain for immutable record-keeping, while zk-SNARK-based proofs enable compliance verification without revealing sensitive underlying data. A prototype was implemented using Solidity smart contracts and Python-based zk-SNARK circuits. Experimental evaluation across varying record sizes, from 50 to 200, demonstrates high security and efficiency, achieving 100% success in detecting simulated tampering attempts. Performance metrics indicate a highly scalable system with an average end-to-end latency of approximately 0.33 seconds, rapid proof generation times of approximately 0.0002 seconds, and near-constant verification times averaging 0.027 seconds. Furthermore, the system maintains a consistent simulated transaction cost of 20.40$ per proof, regardless of the total records processed. Overall, the proposed approach provides a robust, scalable, and computationally efficient solution for modern agri- food supply chains, successfully balancing data confidentiality with rigorous cryptographic integrity.
Open access
Food Supply Chain Traceability
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Digital systems increasingly rely on user location data, raising significant privacy concerns. This study proposes a privacy-preserving location data utilization system that eliminates the need for dedicated base stations by integrating blockchain technology with zero-knowledge proof scheme. Our system converts data from smartphone trajectory data into zero-knowledge proof values and records only these proof values on the blockchain. Thus, the system enables verification of user movement without revealing sensitive information. By integrating the entire process with smart contracts on the blockchain, our system automates transaction processing and monetary transfers without relying on any specific organization. We conduct an experimental evaluation on the blockchain using trajectory data collected from a smartphone application.
Pankaj Kumar, Arun K H, Yogesh N, Prakash Babu · 8 authors
The growing dependance on data-based decisionmaking in healthcare has brought attention to the vital importance of secure, privacy-preserving and collaborative learning techniques. Traditional centralized learning approaches in medical data often raise concerns regarding patient privacy data leaks and even regulatory troubles. Federated learning came as a good solution, where it allows model training in different hospitals without sharing the sensitive patient data. However, federated learning has its problems - it can be mislead with fake updates, the model can even be poisoned and it is really hard to trust every participants involved. In this work, we present fed-chain, a secure and scalable framework which brings together federated learning, blockchain and zero-knowledge-proofs(ZKPs) preserving the privacy of patient's data in healthcare. Blockchain here adds decentralized trust, immutability and makes model updates transparent to review while ZKPs helps in proving correctness without leaking personal data. We are implemented this framework for heart disease prediction where multiple hospitals train the model together but the data stays confidential. Our experimental results shown better accuracy, more strength against attacks and even low communication cost compared to other FL setups. Overall, the systems gives a safer approach for working together on healthcare data, allowing hospitals and research centers to generate valuable predictions using these models while keeping the patient data private and safe.
In Vertical Federated Learning (VFL), prior work has primarily focused on protecting data privacy, while overlooking the risk that participants may manipulate local model execution to mount integrity attacks.Integrating zero-knowledge proofs (ZKPs) into the training process can ensure that each party's computations are verifiable without revealing private data.However, directly encoding deep model training as a monolithic ZKP circuit is impractical due to: (i) complex circuit design and high overhead from frequent parameter commitments, (ii) expensive proof generation for embeddings(cross-party information interface), and (iii) synchronous proof generation that blocks iterative training rounds.To address these challenges, we present ZKSL, an efficient and asynchronous VFL framework that achieves verifiable training under a malicious threat model.ZKSL partitions deep neural networks into layer-wise circuits and generates their proofs in parallel, ensuring input-output consistency via Privacy-Commitment PLONK (PC-PLONK), a lightweight extension that supports low-cost, iteration-by-iteration parameter commitments.For embedding layers, ZKSL adopts a probabilistic verification technique that reduces proof complexity from O(N nd) to O(nd).Furthermore, ZKSL incorporates an asynchronous compute-prove scheduling mechanism to decouple proof generation from training iterations, effectively mitigating pipeline stalls.Experimental results on DeepFM and CNN models show that ZKSL reduces proof generation time by up to 73% while maintaining 99.4% accuracy, demonstrating superior scalability and practicality for real-world federated learning.
In the digital economy era, the contradiction between data sharing and privacy protection is increasingly prominent. Traditional privacy protection technologies such as anonymization, differential privacy, and homomorphic encryption are difficult to meet the diverse privacy demands in multiple fields due to their flaws such as vulnerability to cracking, sacrificing data accuracy, and high computational complexity. Zero-knowledge proof (ZKP), with its core advantage of "data available but not visible", has become the key technical support to solve this contradiction. However, the current protocol types are complex and the demands in various fields vary significantly, leading to confusion in protocol selection and imbalance in resource allocation in practice. This paper systematically reviews the theoretical basis, technical system and mainstream protocol characteristics of zero-knowledge proof, integrates the advantages of demand-driven decision tree and protocol three-dimensional classification model, constructs a protocol selection framework of "demand-driven - feature matching - dynamic optimization", and clarifies the multi-stage decision-making process; Four typical fields, namely education, machine learning, finance, and healthcare, were selected to verify the effectiveness and universality of the framework, providing standardized tools for the large-scale application of zero-knowledge proof technology in various fields and direction guidance for the development of privacy protection technology in the post-quantum era.
Alen Horvat, Steffen Schwalm, Johannes Sedlmeir, Hakan Yildiz
Among the privacy-enhancing technologies explored in the context of the European Digital Identity (EUDI) Wallets, zero-knowledge proofs stand out for their ability to maintain established levels of cryptographic verifiability while enabling data minimisation – relative to the requirements expressed by the relying party (RP). However, legal frameworks in many sectors require the collection of verifiable data beyond the RP’s immediate needs, which may substantially narrow down the scope of data minimisation that can be achieved in regulated domains. Accordingly, this paper examines the tensions between the strict data minimisation requirements for the EUDI Wallets and the extensive legal proof obligations that relying parties must fulfil. Our analysis of the regulatory foundations and relevant technical mechanisms identifies documentation, audit, and long-term preservation obligations as key sources for friction. We explore the implications of the corresponding tensions, point to gaps in current standardisation and compliance regimes, and suggest potential technical and non-technical solution approaches that could help reap the benefits advanced privacy-enhancing technologies can offer in practice.
As vehicles become increasingly intelligent, the Internet of Vehicles (IoV) is gaining widespread popularity. However, security concerns in the IoV are becoming increasingly critical, particularly regarding vehicle identity authentication, which directly affects system stability and trust. Therefore, designing an efficient and secure authentication mechanism has become an essential challenge in IoV development. This study focuses on the identity authentication problem between vehicles and roadside units (RSUs) in the IoV. To address the computational overhead challenges posed by the rapid increase in the number of vehicles, we propose a lightweight authentication scheme based on a zero-knowledge proof and design a batch verification mechanism for it. Furthermore, to address the dynamic nature of vehicular traffic, we introduce a Federated Intelligent Dynamic Batching Algorithm (FIDBA) based on a lightweight Multi-Layer Perceptron (MLP). This algorithm dynamically predicts the optimal batch size to minimize verification latency and computational cost. This approach ensures security while significantly reducing computational and communication costs, improving scalability and real-time performance. To further address delays in the handover process, we are integrating blockchain technology. Leveraging its decentralized, immutable, and transparent nature, the blockchain securely stores authenticated vehicle information, avoiding repeated verifications and reducing handover delays. To mitigate inefficiencies in blockchain queries, we design a caching mechanism that enables fast data access. Through theoretical analysis and experimental validation, this study demonstrates the superiority of the proposed scheme in security, efficiency, and scalability. This scheme meets current IoV needs and provides a promising reference for future intelligent transportation systems.
Federated learning (FL) enables collaborative model training while preserving data privacy, yet both centralized and decentralized approaches face challenges in scalability, security, and update validation. We propose ZK-HybridFL, a secure decentralized FL framework that integrates a directed acyclic graph (DAG) ledger with dedicated sidechains and zero-knowledge proofs (ZKPs) for privacy-preserving model validation. The framework uses event-driven smart contracts (EDSCs) and an oracle-assisted sidechain to verify local model updates without exposing sensitive data. A built-in challenge mechanism efficiently detects adversarial behavior. In experiments on image classification and language modeling tasks, ZK-HybridFL achieves faster convergence, higher accuracy, lower perplexity, and reduced latency compared to Blade-FL and ChainFL. It remains robust against substantial fractions of adversarial and idle nodes, supports sub-second on-chain verification with efficient gas usage, and prevents invalid updates and orphanage-style attacks. This makes ZK-HybridFL a scalable and secure solution for decentralized FL across diverse environments.
Ahmet Ramazan Ağırtaş, Arda Buğra Özer, Zülfükar SAYGI, Oğuz Yayla
Unbiased and unpredictable randomness is a cornerstone of Web3 security, underpinning everything from consensus protocols to DeFi logic. Although Distributed Verifiable Random Functions (DVRFs) eliminate central points of failure, current designs often have to compromise performance. Most existing protocols are hindered by one of three limitations: proofs that scale linearly with the number of participants, high computational cost of bilinear pairings, or latency introduced by mandatory interactive steps during generation. In this work, we present Icy-DVRF, a protocol that improves DVRFwCP by employing a preprocessing scheme similar to FROST to reduce the number of interaction rounds among participants and lowering the additional communication cost from <inline-formula> <tex-math notation="LaTeX">$O(n^{2} t)$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$O(t)$ </tex-math></inline-formula> while maintaining constant-size proofs. The downside of our construction is that, relative to DDH-DVRF and GLOW-DVRF, this approach incurs an additional off-chain communication round due to the threshold structure of our non-interactive zero-knowledge proof. This architecture ensures that verification costs remain low, regardless of the set of participants. While theoretical estimates suggest verification costs of approximately one quarter of those of standard designs, our empirical benchmarks on the Sepolia testnet, utilizing the EIP-2537: Precompile for BLS12-381 curve operations, confirm that Icy-DVRF requires only 88,803 gas for full execution. This represents a significant 43.02% reduction in total gas consumption compared to existing pairing-based constructions, saving 67,035 gas per on-chain verification. Off-chain, eliminating DVRFwCP’s Augmented Secure-DKG round yields a per-node speedup ranging from a factor of 1.46 at <inline-formula> <tex-math notation="LaTeX">$(n,t)=(5,3)$ </tex-math></inline-formula> to a factor of 4.43 at <inline-formula> <tex-math notation="LaTeX">$(n,t)=(50,34)$ </tex-math></inline-formula>.
Open access
Generative Adversarial Networks and Image Synthesis
Dr.B.Swathi Dr.B.Swathi, MOHAMMAD SANA, DAMERUPPULA SAI KIRAN, JADI GANESH · 5 authors
The quick rise of digital technologies has shown how blockchain could improve business operations by making them safer, more open, and less centralized. Most blockchain solutions, on the other hand, are made for big businesses, which makes it hard for small and medium-sized businesses (SMEs) to use them because they are too expensive, too complicated, and not modular. This study suggests a blockchain-based framework designed specifically for small and medium-sized businesses (SMEs) to make digital transformation more affordable. The framework includes stable consensus protocols, governance mechanisms, and important services like Decentralized Identity (DID), Zero-Knowledge Proofs (ZKP), and Digital Asset Management (DAM). It is meant to be modular, scalable, and simple to connect to current business systems. Experimental testing shows that SMEs are more efficient, secure, and easy to use. The proposed framework lowers the barriers to entry and lets small and medium-sized businesses use blockchain for new ideas, better operations, and safe online transactions.
In the face of the regulatory failure problem caused by blockchain hidden addresses, existing solutions often fall into a dilemma where 'privacy protection' and 'compliance review' are either one or the other.This paper proposes an innovative integration framework that transforms the behavioural elements in anti-money laundering and other legal provisions (such as 'high-frequency and small-scale transactions') into computable logic.Based on zero-knowledge proof technology, it generates verifiable credentials to determine whether the transaction behaviour is compliant without revealing the true identity of the address.Experiments on a public blockchain transaction dataset (elliptic) show that this framework achieves an average improvement of over 15% in core identification performance compared to traditional non-private rule-based methods, while maintaining an acceptable performance overhead.As a proof-of-concept validation conducted on a transparent dataset with simulated concealment, the actual performance may differ in native privacy-preserving chains.This research provides a new approach that combines legal rigor with technical feasibility for achieving effective on-chain behaviour supervision while protecting user privacy.