Follow blockchain research across journals, conferences, and preprint repositories.
5,430 results · page 36 of 227
Yuhui Zhang, Yang Feng, Huijian Han, Yichao Ma · 7 authors
No abstract is available for this record.
Samuel Polgar
Digital credential wallets manage identity documents such as government IDs and financial certificates, face the trilemma of privacy, security, and usability. Optimizing for anonymity by using Anonymous Credentials enhances privacy, but introduces challenges. Current benchmarks show verification using zero-knowledge proofs of knowledge taking 50–500ms, far exceeding the <1ms of standard credentials, impeding usability. Additionally, anonymity complicates security: preventing multiple-credential issuance (sybil resistance) or enforcing revocation becomes difficult when both users and objects are essentially secret. These issues are urgent due to the EU’s 2026 mandate for EU-wide credential wallet usage, which will drive widespread adoption of digital credential wallets, while critical use cases, like privately combining credentials from multiple issuers for KYC, emphasize the importance of this work. This thesis extends existing work and develops new, fast cryptographic primitives for privacy preserving credential wallets. It introduces the fastest anonymous credential scheme with a 3.77ms Show+Verify time for 10 attributes, outperforming prior methods by 10-15%. Three extensions enhance this scheme. 1) formalized Identity Binding property for secure multi-issuer, multi-credential verification, with an implementation verifying 16 credentials from unique issuers in 72ms; 2) new nullifier constructions using Σ-protocols without pairings, improving privacy-preserving sybil resistance by 5x over previous approaches; 3) T-SIRIS, a threshold-issued, sybil-resistant identity system with near-constant Show+Verify times, over 30x faster than comparable systems [RAR+24]. These advancements are validated by an open-source Rust benchmarking library, delivering standardized empirical data across anonymous credential schemes.
Shangping Wang, Qi Huang, Ruoxin Yan, Juanjuan Ma · 5 authors
No abstract is available for this record.
Abdelrazak A. Yousef Elbunan, Nuradeen K. Emhemed Fethalla, Badriya Abdullah Altarhuni
The rapid proliferation of autonomous robotic systems, ranging from nano-drones to industrial collaborative robots (cobots), is generating massive, distributed datasets. While deep learning (DL) serves as the cornerstone of modern robotic intelligence, the conventional approach of centralizing this data for training poses insurmountable challenges related to privacy, security, bandwidth, and latency. Federated Learning (FL) has emerged as a disruptive paradigm that enables collaborative model training across distributed devices without the need for raw data exchange. However, the integration of FL into real-world robotic swarms—characterized by extreme heterogeneity, dynamic connectivity, and stringent resource constraints—introduces a unique set of complexities that extend far beyond those of conventional edge devices. This survey provides a comprehensive and critical examination of the burgeoning field of FL within robotic and autonomous systems. We move beyond a mere overview to present a novel taxonomy that classifies FL architectures for robotics based on communication topology, learning paradigm, and application criticality. A significant portion of our analysis is dedicated to the potent synergy between FL and Distributed Ledger Technologies (DLTs), particularly blockchain, for achieving decentralized trust, auditability, and robust aggregation in the presence of potentially malicious agents. We extensively review applications across perception, control, and collaborative tasks, highlighting pioneering works in multi-robot SLAM, federated reinforcement learning, and human-robot interaction. Furthermore, we identify and discuss pressing open challenges, including communication efficiency in mobile swarms, energy-aware client selection, personalized learning for non-IID data, and defense mechanisms against sophisticated adversarial attacks. This paper serves as a foundational reference for researchers and practitioners aiming to develop the next generation of private, secure, and collectively intelligent robotic systems.
Mohammad Bilal Aziz, Ali Shah Naushad, M. Umair Siddiqui, Jawwad Ahmed Shamsi
No abstract is available for this record.
Changjin Zhao, Xiang Feng, Huiqun Yu
No abstract is available for this record.
M. V. Sanand, J Sathish Kumar
No abstract is available for this record.
Subash Ranjan Kabat
The rapid adoption of Artificial Intelligence (AI) across industries, particularly in healthcare, finance, and smart devices, has introduced significant concerns regarding data privacy, security, and compliance with regulations such as GDPR, HIPAA, and CCPA. Traditional centralized machine learning (ML) models require large-scale data aggregation, increasing risks of data breaches, misuse, and unauthorized access. Federated Learning (FL) has emerged as a transformative solution, allowing multiple edge devices or organizations to collaboratively train machine learning models without sharing raw data. This paper explores the principles, advantages, and challenges of FL and conducts an empirical analysis comparing FL’s efficacy, security, and scalability to centralized models. A case study on federated learning in healthcare diagnostics highlights the real-world impact of this approach. Additionally, insights from a structured survey of AI researchers, data scientists, and industry professionals are analyzed to assess FL adoption, technical challenges, and future potential. Findings suggest that FL enhances privacy and compliance, making it particularly suitable for industries handling sensitive information. However, challenges such as high computational costs, model convergence issues, and communication overhead must be addressed for FL to achieve widespread adoption. Future advancements in efficient federated learning frameworks, regulatory standardization, and privacy-preserving AI techniques will further define FL’s role in the evolution of decentralized artificial intelligence.
Ahmad Musamih, Khaled Salah, Raja Jayaraman, Samer Ellahham · 6 authors
Non-fungible tokens (NFTs) are unique digital assets stored on blockchains. NFTs are ideally suited for tokenizing genomic data, as they empower individuals with complete control over them. Next-generation sequencing (NGS) technology creates repositories of sequenced data from individuals’ raw genomic data, which raises challenges related to data ownership, management, and secure sharing. In this paper, we propose a blockchain and NFT-based solution that addresses the challenges of managing, sharing, and monetizing genomic data while preserving privacy using Threshold Cryptography and Fully Homomorphic Encryption (FHE). We integrate the proposed solution with the Interplanetary File System (IPFS), a decentralized storage system, to handle the substantial amount of genomic data off-chain. We develop three smart contracts to facilitate genomic data management, sharing, and monetization. We introduce composable NFTs to ensure that sequenced genomic data (SGD) NFTs are always linked to the parent raw genomic data (RGD) NFTs to maintain traceability. We present various diagrams and algorithms to illustrate the functionality of our solution. Our testing and validation results demonstrate that smart contracts function as intended. The cost evaluation shows that implementing the solution on a private blockchain is more feasible and user-friendly. Our solution provides a comprehensive framework for genomic data management, sharing, and monetization, with privacy-preserving mechanisms and traceability. We provide guidelines for the generalizability of our solution beyond genomics and outline the challenges and limitations of the proposed solution. We make the source code of the smart contracts publicly available on GitHub.
Aqeel A. Yaseen, Kalyani Patel, Abdulla J. Y. Aldarwish, Ali A. Yassin
No abstract is available for this record.
Kaya Kuru, Kaya Kuru, Kaan Kuru, Kaan Kuru
It is anticipated that cybercrime activities will be widespread in the urban metaverse ecosystem due to its high economic value with new types of assets and its immersive nature with a variety of experiences. Ensuring reliable urban metaverse cyberspaces requires addressing two critical challenges, namely, cybersecurity and privacy protection. This study, by analysing potential cyberthreats in the urban metaverse cyberspaces, proposes a blockchain-based Decentralised Privacy-Preserving Machine Learning (DPPML) authentication and verification methodology, which uses the metaverse immersive devices and can be instrumented effectively against identity impersonation and theft of credentials, identity, or avatars. Blockchain technology and Federated Learning (FL) are merged in the developed DPPML approach not only to eliminate the requirement of a trusted third party for the verification of the authenticity of transactions and immersive actions, but also, to avoid Single Point of Failure (SPoF) and Generative Adversarial Networks (GAN) attacks by detecting malicious nodes. The developed methodology has been tested using Motion Capture Suits (MoCaps) in a co-simulation environment with the Proof-of-Work (PoW) consensus mechanism. The preliminary results suggest that the built techniques in the DPPML approach can prevent unreal transactions, impersonation, identity theft, and theft of credentials or avatars promptly before any transactions have been executed or immersive experiences have been shared with others. The proposed system will be tested with a larger number of nodes involving the Proof-of-Stake (PoS) consensus mechanism using several other metaverse immersive devices as a future job.
Tariq Qayyum, Zouheir Trabelsi, Asadullah Tariq, Mohamed Adel Serhani · 6 authors
The emergence of large-scale quantum computers threatens the security of classical public-key cryptosystems, making it essential to adopt post-quantum (PQ) security in Intelligent Transportation Systems (ITS). We introduce a framework that blends quantum-resilient cryptographic primitives with a sharded blockchain architecture. Each shard maintains a local ledger for its vehicle group, enabling real-time transactions and efficient certificate management without overloading any single chain. A lightweight global chain periodically anchors all shards, preserving system-wide consistency and blocking malicious revocations. Vehicles register or revoke PQ credentials via a lightweight Proof-of-Stake consensus, while roadside units (RSUs) handle signature verification to offload on-board computation. We further demonstrate practicality through a federated-learning case study in which vehicles exchange signed model updates over the same secure channel. SUMO/TraCI simulations with 2 000 vehicles and 10 shards show that despite PQ overhead the system sustains near real-time delays and high throughput. The framework thus offers a decentralized, quantum-resilient solution for secure Vehicle-to-Everything communications in next-generation ITS.
Venkata Naga Lakshmi Likhitha Paruchuri, Sandeep Kumar Panda, Dileep Kumar Murala
No abstract is available for this record.
Kaarel August Kurik, Peeter Laud
No abstract is available for this record.
Dan Boneh, Aditi Partap, Brent Waters
No abstract is available for this record.
Farhana Javed, Josep Mangues‐Bafalluy, Engin Zeydan, Luis Blanco
This paper proposes a blockchain-enabled framework to enhance trust, transparency, and collaboration in Open Radio Access Network (O-RAN) infrastructures through Federated Learning (FL). Traditional O-RAN architectures and centralized machine learning approaches face challenges when integrating multi-vendor environments, primarily due to lack of trust, proprietary data concerns, and limited interoperability. Our solution transitions from implicit trust, where the reliability of contributions is assumed, to explicit trust, where reputation is verifiably established on-chain. We introduce a blockchain-based reputation mechanism that evaluates the accuracy, integrity, and quality of participants’ model updates within the FL process. Smart contracts automate critical tasks-such as participant registration, model update verification, and reputation scoring-ensuring that data inputs directly influence accountability in a tamper-proof, transparent manner. By deploying the framework on a scalable Layer 2 blockchain (Polygon) testnet and proposing the use of a blockchain oracle within this architectural framework for secure off-chain computations, this work focuses on a conceptual architectural approach by aligning with O-RAN’s architecture to propose and deploy a Decentralized Application (DApp) on the blockchain. The proposed framework emphasizes a conceptual design over performance optimization and is structured to naturally benefit from ongoing improvements in blockchain scalability, which may reduce latency and enhance operational efficiency over time. Smart contracts for crucial processes and reputation calculation are included within our proposed DApp. The implementation of this work is publicly accessiblehttps://github.com/farhanajaved/Reputation_O-RAN.
Bora Buğra Sezer, Sedat Akleylek, Urfat Nurıyev
Blockchain technology has produced effective solutions and provides security by using cryptographic tools for various applications, attracting attention from the academic community. Therefore, researchers have taken advantage of the features of blockchain technology to increase the security of the ecosystem. Recently, as the existence of quantum computers has been felt, researchers have started to benefit from post-quantum cryptography to increase privacy and security. There has been an increase in data and asset protection in post-quantum blockchain-based solutions. To the best of our knowledge, there is no comprehensive review or taxonomy that provides a complete picture of post-quantum secure structures with privacy-preserving techniques that have the potential to be used in blockchain. This paper aims to close this gap by systematically examining these approaches and revealing the deficiencies in the existing literature and the development potential in these areas. The taxonomy examines the role of blockchain technology in post-quantum cryptography and emphasizes the potential of technologies such as zero-knowledge proof to ensure privacy in post-quantum blockchain-based systems. We also review the existing literature on addressing the performance overhead, interoperability, scalability, and security challenges in implementing post-quantum cryptography in zero-knowledge proof-enabled blockchain architectures that protect against quantum computing threats. The studies are collected from journal papers in widely used academic databases between 2018 and 2024. The studies are subjected to certain elimination criteria, and 13 studies are reviewed in detail. Our approach will facilitate discussions on future research directions by proposing the accessibility of post-quantum cryptography against quantum threats to blockchain systems and solutions to the challenges that arise in the integration phase.
Nicolò Romandini
In today's data-driven world, vast amounts of information power Machine Learning (ML) models for a wide range of applications. However, this data flow raises significant privacy concerns, as individuals are often reluctant to share personal information, especially given increasing regulations on data protection. Federated Learning (FL) offers a solution by training ML models directly on users' devices and sending only model updates to a central server. This distributed approach enables collaboration without sharing personal data, but challenges remain. Centralization may lead to server bottlenecks, reduced resilience, and fairness concerns if updates from certain devices are prioritized. Additionally, the lack of transparency and accountability can erode trust, while security risks, such as data poisoning and model inversion attacks, further complicate FL. Deployment can be costly and time-consuming, and participants may also lack incentives. Regulatory compliance, such as ensuring the right to be forgotten, adds complexity, as removing data from FL models without full retraining is challenging. This dissertation proposes integrating Distributed Ledger Technologies (DLTs) with FL to address these challenges. DLT decentralizes the aggregation process, enhancing security, transparency, and fairness through immutable record-keeping and traceability. Two DLT-based architectures are presented: one blockchain-based and the other using a Directed Acyclic Graph (DAG) for scalability. These approaches utilize Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) to track contributions and verify participants. Furthermore, a DLT-based FL as a Service (FLaaS) is introduced to simplify deployment, incorporating model validation to mitigate poisoning attacks and token-based incentives to encourage participation. Additionally, this dissertation outlines design guidelines for Federated Unlearning (FU), covering key evaluation metrics, existing techniques, and future research. Finally, a new unlearning algorithm is proposed to address adversarial settings and protect model integrity. These contributions pave the way for more secure, transparent, and resilient FL systems that can meet the needs of next-generation data-driven applications.
G. Sowmya Bala, P. S. G. Aruna Sri, Satyanarayana Korada, Suneel Gone
Traditional ticketing systems are at risk of fraud, counterfeiting, and issues concerning scalability. In this research, we investigate the application of blockchain technology towards the revolutionary concept of event tickets. We analyze how fundamental attributes of blockchain technology, such as
Haojie Yin, Shuhong Chen, Zhenkun Luo, Mengmeng Tang · 6 authors
No abstract is available for this record.
Charlotte Hoffmann, Krzysztof Pietrzak
No abstract is available for this record.
Pallavi Arora, Arya Tapikar, Akshat Aryan, V Amogh Manish · 5 authors
No abstract is available for this record.
Dan Bogdanov, Eduardo Brito, Annika Jaakson, Peeter Laud · 5 authors
Abstract This paper introduces a new set of privacy-preserving mechanisms for verifying compliance with location-based policies for vehicle taxation, or for (electric) vehicle (EV) subsidies, using Zero-Knowledge Proofs (ZKPs). We present the design and evaluation of a Zero-Knowledge Proof-of-Location (ZK-PoL) system that ensures a vehicle’s adherence to territorial driving requirements without disclosing specific location data, hence maintaining user privacy. Our findings suggest a promising approach to apply ZK-PoL protocols in large-scale governmental subsidy or taxation programs.
Hyoseok Jang, Sangmoon Lee, Haneol Cho, Chansoo Kim
No abstract is available for this record.