Blockchain Papers

José F. D. Venturini, Alex E. G. Leite, Fabiano Hessel

No abstract is available for this record.

Puneet Bakshi, Siddhant Bopche, M. Vinodh Kumar

No abstract is available for this record.

Amrutanshu Panigrahi, Amaresh Parida, Abhilash Pati, Bibhuprasad Sahu · 6 authors

No abstract is available for this record.

Octavian Untila

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.

Namrata Jangam, Hazra Shaikh, Shraddha Pawar, Zainab Shaikh · 5 authors

No abstract is available for this record.

Nai‐Wei Lo, Cheng-I Lin, Chih-Chieh Chang, Chi-Yang Chang · 5 authors

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

Zhanlin Wang

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Pal Krishan, Kishor Amit

No abstract is available for this record.

Christopher Schulze

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.

Chengsheng Yuan, Lvyang Cao, X F Li, Zhili Zhou · 6 authors

No abstract is available for this record.

Priya Patel, Nitesh Sureja

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.

Bianca-Renata Mircea, Iulian Aciobăniței

No abstract is available for this record.

Amit Chakraborty, Sandip Roy, Md Morshed Alam, Debasis Giri · 5 authors

No abstract is available for this record.

Hideaki Miyaji, Hayato Takayama, Hiroshi Yamamoto

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.

Yixiao Zheng, Changzheng Wei, Xiaodong Qi, Hanghang Wu · 12 authors

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.

Ying Ouyang, Yanhong Xu, Zuming Liu, Deng Tang · 5 authors

No abstract is available for this record.

Raj Mehta, Prasham Shah, Pimal Khanpara

No abstract is available for this record.

Fan Gao

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.

Wei‐Kuo Chiang, Chia-Jui Liu

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.

Ningyuan Chen, Chiew Foong Kwong, David Chieng, Pushpendu Kar · 8 authors

This paper addresses the pivotal issue of privacy in traffic condition assessment within Cellular Vehicle-to-Everything (C-V2X) and Intelligent Transportation Systems, specifically targeting applications that do not have stringent low-latency requirements. Despite significant advancements in the field, existing approaches often fail to provide robust privacy protection without compromising network efficiency and data integrity. Our study is motivated by the pressing need to overcome these limitations through solutions that enhance network reliability, data privacy, and node reputation management. At the core of our approach is the implementation of Zero-Knowledge Proofs (ZKPs), which facilitate the secure verification of vehicular data while safeguarding individual privacy. We developed a method for aggregating ZKPs to improve data processing efficiency, thereby substantially reducing network load. Moreover, our application of machine learning techniques for node trustworthiness assessment further strengthens network integrity. The integration of blockchain technology in our framework addresses the traditional centralisation challenges in C-V2X, particularly in the areas of data storage, processing, and verification, thereby enhancing the network's security and resilience. Our framework bridges these gaps, resulting in a 90% reduction in computation and storage costs on-chain compared to a non-aggregated benchmark where each proof is submitted individually. Additionally, the node trustworthiness assessment reduces network delay by up to 31.7%.

Jeffery Podolski

No abstract is available for this record.

Subhasis Thakur, John Breslin

No abstract is available for this record.