The Internet of Things (IoT) demands robust mechanisms for secure communication and trust establishment among connected devices. Traditional Public Key Infrastructure (PKI) solutions face limitations in scalability, centralization and single points of failure. These limitations hinder their effectiveness in dynamic IoT environments. To address these challenges, this paper introduces a new decentralized authentication protocol for secure identity management and data exchange in IoT, called ISIF (IOTA-Assisted Self-Sovereign Identity Framework). This framework is based on Self-Sovereign Identity (SSI) principles and leverages Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) to enable mutual authentication without relying on centralized authorities. DIDs ensure decentralized identity management and VCs provide verifiable context-specific claims. This dual-layer approach enables robust and attribute-based authentication, which reduces the risk of unauthorized access and improving interoperability in decentralized IoT environments. ISIF employs the IOTA Tangle as a distributed ledger to manage and verify DIDs and VCs. This offers a decentralized, immutable record that supports efficient and tamper-resistant identity management. ISIF ensures that all interactions within the IoT network are securely authenticated and resilient to tampering. The experimental results show that the framework maintains efficient DID generation and VC issuance times even as network size scales, overcoming the bottlenecks inherent in PKI-based systems. Experimental results demonstrate that ISIF maintains efficient DID generation and VC issuance, even as network size scales. Experimental results show that DID generation time increases from 1.85 ms (for 50 nodes) to 10.81 ms (for 250 nodes), while VC issuance time ranges from 2.66 ms to 13.21 ms. Similarly, VC verification time increases from 3.54 ms to 22.27 ms as the network scales. Despite these increases, the overall end-to-end (E2E) delay remains low (0.16–0.33 ms), ensuring efficient real-time authentication. These findings confirm ISIF’s feasibility for large-scale IoT authentication without performance degradation. Furthermore, the IOTA Tangle’s performance in handling varied payload sizes affirms its suitability for managing block generation and retrieval in IoT, ensuring practical processing times that uphold security and decentralization.
Omar Isaac Asensio, Catherine E. Moore, Nícola Ulibarrí, Mecit Can Emre Simsekler · 6 authors
Abstract Data for Policy ( dataforpolicy.org ), a trans-disciplinary community of research and practice, has emerged around the application and evaluation of data technologies and analytics for policy and governance. Research in this area has involved cross-sector collaborations, but the areas of emphasis have previously been unclear. Within the Data for Policy framework of six focus areas, this report offers a landscape review of Focus Area 2: Technologies and Analytics. Taking stock of recent advancements and challenges can help shape research priorities for this community. We highlight four commonly used technologies for prediction and inference that leverage datasets from the digital environment: machine learning (ML) and artificial intelligence systems, the internet-of-things, digital twins, and distributed ledger systems. We review innovations in research evaluation and discuss future directions for policy decision-making.
Abstract We present two simple zero-knowledge interactive proofs that can be instantiated with many of the standard decisional or computational hardness assumptions. Compared with traditional zero-knowledge proofs, in our protocols, the verifier starts first, by emitting a challenge, and then, the prover answers the challenge.
Istiaque Ahmed, Kentaroh Toyoda, Tadashi Nakano, Thi Hong Tran
Traditional digital identity systems struggle with centralization, vulnerability to manipulation, and a lack of transparency. In distributed identity, different cryptographic methods are used for issuing credentials, that create challenges during presentation. It suffer from a fundamental interoperability barrier with heterogeneous digital-signature schemes, forcing each verifier either to implement every scheme or to trust a central translation gateway. We propose a signature-agnostic verification framework that eliminates this barrier. The core idea is to commit a salted root hash of credential claims to a distributed ledger and ensure the authenticity using a smart contract. A zero-knowledge proof (zk-SNARK) is used to prove a selected claim set without revealing actual information. The verification reduces to a single hash-consistency check, and the verifier never touches issuer-specific signatures. A pleasant side effect is that the same verifiable presentation (VP) can be reused across verifiers and sessions, since trust derives from the on-chain anchor rather than transient signatures. This research will advance the identification ecosystem, enabling applications such as eKYC across finance, healthcare, and other sectors. We implement our method on Ethereum Virtual Machine (EVM) using Groth16, benchmark gas cost, proof size, and latency, and show its feasibility and computational efficiency. The privacy and security analysis confirms that the proposed solution is resistant to various attacks.
Advances in Internet of Medical Things technology, information and communication technologies, and machine learning have initiated the shift in healthcare towards smart healthcare. Centralization of health data to train ML models does pose privacy, ownership, and regulatory problems. Federated learning solves such problems by distributing the learning process to several devices, but it also encounters problems like encouraging participants and model aggregation correctness. Combining blockchain and FL can solve such problems through a decentralized approach that provides greater security and privacy for intelligent healthcare. This survey provides a systematic review of blockchain-based federated learning (BCFL) systems in healthcare. Key design features of BCFLs are analyzed, such as consensus protocols, crypto protocols, storage topology, and integration processes relevant to healthcare use cases. Characteristics such as convergence delay, computation overhead, accuracy loss when privacy is an issue, and ledger scalability for different implementations are compared among common implementations. The works of recent FL-based healthcare frameworks have been discussed along with determining the challenges and research directions for healthcare use cases.
Seid Mehammed, Girma Bewuketu, Demeke Getaneh, Md Nasre Alam · 6 authors
We present a permissioned blockchain–audited federated learning (FL) framework that strengthens data provenance and model‐update integrity. Our contribution is primarily engineering and architectural: a modular two‐channel design (provenance vs. update‐audit), lightweight on‐chain validation with off‐chain analytics, and a practical mapping to the 1 + 5 architectural views. In a TensorFlow Federated + Hyperledger Fabric prototype with 10 clients, we observe ≈18% faster anomaly detection under attack and a + 0.4 pp accuracy delta versus a baseline FL setup, with ~6% communication and ~8% energy overhead. We also provide a proof‐of‐concept zero‐knowledge succinct noninteractive argument of knowledge (zk‐SNARK) flow to validate per‐client summary properties off‐chain while anchoring results on‐chain. These contributions collectively advance the practical deployment of secure, auditable FL systems.
Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive, in which a prover convinces a verifier that a given statement is true without leaking any additional information.However, existing zkSNARKs suffer from high computation overhead in the proof generation.This limits the applications of zkSNARKs, such as private payments, private smart contracts, and anonymous credentials.Private delegation has become a prominent way to accelerate proof generation.In this work, we propose Siniel, an efficient private delegation framework for zkSNARKs constructed from polynomial interactive oracle proof (PIOP) and polynomial commitment scheme (PCS).Our protocol allows a computationally limited prover (a.k.a.delegator) to delegate its expensive prover computation to several workers without leaking any information about the private witness.Most importantly, compared with the recent work EOS (USENIX'23), the state-of-the-art zkSNARK prover delegation framework, a prover in Siniel needs not to engage in the MPC protocol after sending its shares of private witness.This means that a Siniel prover can outsource the entire computation to the workers.We compare Siniel with EOS and show significant performance advantages of the former.The experimental results show that, under low bandwidth conditions (10MBps), Siniel saves about 16% time for delegators than that of EOS, whereas under high bandwidth conditions (1000MBps), Siniel saves about 80% than EOS.
Natalia Borgoñós García, María Hernández Padilla, Antonio Fernando Skarmeta Gómez
Abstract Data Spaces are ecosystems designed to allow multiple organizations or companies to share data in a secure manner. Despite the potential of these technologies, they encounter a number of challenges and privacy issues that limit their use. Privacy Preserving Enablers are mechanisms developed to tackle these difficulties, ensuring data integrity and access control. This paper aims to analyze the role of some Privacy Preserving Enablers and its integration with Connectors in the context of Data Spaces. The research will focus on key enablers, including a Self-Sovereign Identity with Zero-Knowledge Proof, which is a privacy preserving approach that allows users to verify their identity and attributes without the need to disclose underlying data, ensuring their privacy. Additionally, the usage of Sticky Policies instantiated through Attribute-Based Encryption attaches control policies into the encrypted data in order to have an attribute-based access control, enhancing its security. The application of Policies Enforcement assure the consistent application of policies and the maintenance of the security within the Data Space.
Dadmehr Rahbari, Masoud Daneshtalab, Maksim Jenihhin
With the rapid growth of edge AI applications, there is an increasing demand for federated learning (FL) frameworks that are both efficient and privacy-preserving. This work introduces a robust approach that leverages homomorphic encryption (HE) to ensure data confidentiality during decentralized training. To tackle the typical challenges of FL—such as high communication overhead, resource limitations, and convergence inefficiencies—the method integrates dynamic client clustering, quantization-aware training, and structured model pruning. These optimizations collectively reduce latency and memory consumption while accelerating model convergence. Evaluations using the Human Activity Recognition dataset show that the proposed approach outperforms several state-of-the-art FL methods, achieving an average +8.4% improvement in accuracy, -16.2% lower latency, -35.1% reduction in memory usage, and -2.7% lower security overhead. These results demonstrate its suitability for real-time, resource-constrained scenarios in domains like healthcare, IoT, and finance, where maintaining a strong balance between efficiency and privacy is essential.
The rapid advancement of technology and the widespread adoption of various IoT devices have resulted in significant challenges in handling and analyzing the vast and diverse volumes of data they produce. Conventional machine learning methodologies, which require data aggregation in a centralized repository for analysis, are often unfeasible due to the significant data volume, limited communication bandwidth, and strict security and privacy requirements. Federated Learning (FL) presents an innovative resolution by facilitating data analysis to take place directly at the data origin, federating the outcomes to generate results comparable to centralized processing. Due to the advancements in growth of federated learning architecture, a number of open-source frameworks have been established to implement this strategy. These frameworks facilitate a range of applications, including healthcare and finance, as well as IoT and smart city initiatives, by enabling data to remain decentralized while still supporting the development of comprehensive analytical models. This study provides a comprehensive evaluation of these established open-source FL frameworks, examining their applicability across various sectors and highlighting their advantages and limitations. The authors examine the various architectural structures, supported algorithms, level of implementation ease, and community support of each framework. The assessment additionally takes into account the scalability of these frameworks and their flexibility in various data scenarios. Through the assessment of the current capabilities and developmental phases of these FL frameworks, this study aims to support practitioners and researchers in identifying the most suitable tools for their specific needs. This analysis highlights the significance of FL in facilitating secure and effective data analysis in the contemporary technological landscape.
In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data from Industrial Internet of Things (IIoT) devices, have highlighted the critical need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. We defined a storage model and data format for private data through the design and deployment of smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in the real-world simulations, Hyper-IIoT shows well-controlled contract execution time, stable system throughput and fast consensus process, and is capable of handling high throughput and effective consensus in distributed systems even in large-scale request scenarios.
随着工业物联网的快速发展，在分布式环境下高效训练机器学习模型，同时保障数据隐私与系统安全，已成为亟待解决的关键问题。传统联邦学习虽然在一定程度上缓解了数据泄露风险，但仍面临中心化服务器易受攻击、投毒威胁以及模型更新透明度不足等挑战。为此，文章提出了一种基于区块链的去中心化联邦学习框架(BCS-FL)。该框架结合区块链技术与权益证明(PoS)共识机制，实现联邦学习的去中心化训练、筛选及存储，从而提升系统的安全性、透明度和可靠性。具体而言，在本地设备层，各智能工厂(客户端)基于自身数据训练局部模型，并经过裁剪和差分隐私保护后，生成局部模型包上传至验证层；在去中心化验证层，由PoS选出的验证节点计算局部模型间的余弦相似度以及损失函数差值，以筛选可信、高质量的客户端模型；在区块链存储层，矿工节点验证候选全局模型的哈希值、时间戳以及客户端签名，以确保模型版本的一致性，并通过PBFT共识将其存储至区块链，供所有客户端下载与同步，确保模型更新的可追溯性和抗篡改能力。With the rapid development of the Industrial Internet of Things, efficiently training machine learning models in distributed environments while ensuring data privacy and system security has become a critical challenge. Although traditional federated learning mitigates the risk of data leakage to some extent, it still faces challenges such as the vulnerability of centralized servers to attacks, poisoning threats, and insufficient transparency in model updates. To address these issues, this paper proposes a blockchain-based, decentralized, federated learning framework (BCS-FL). By integrating blockchain technology with the Proof-of-Stake (PoS) consensus mechanism, BCS-FL enables decentralized training, selection, and storage of federated learning models, thereby enhancing system security, transparency, and reliability. Specifically, at the local device layer, intelligent factories (clients) train local models based on their own data and generate local model packages after pruning and differential privacy protection before uploading them to the coordination layer. At the decentralized coordination layer, coordination nodes selected through PoS compute the cosine similarity and loss function differences between local models to filter out reliable and high-quality client models. At the blockchain storage layer, miner nodes verify the hash values, timestamps, and client signatures of candidate global models to ensure model version consistency. The final global model is stored on the blockchain through PBFT consensus, allowing all clients to download and synchronize it, ensuring model update traceability and tamper resistance.
With the increasing use of online services, the protection of the privacy of users becomes more and more important. This is particularly critical as authentication and authorization as realized on the Internet nowadays, typically relies on centralized identity management solutions. Although those are very convenient from a user's perspective, they are quite intrusive from a privacy perspective and are currently far from implementing the concept of data minimization. Fortunately, cryptography offers exciting primitives such as zero-knowledge proofs and advanced signature schemes to realize various forms of so-called anonymous credentials. Such primitives allow to realize online authentication and authorization with a high level of built-in privacy protection (what we call privacy-preserving authentication). Though these primitives have already been researched for various decades and are well understood in the research community, unfortunately, they lack widespread adoption. In this paper, we look at the problems, what cryptography can do, some deployment examples, and barriers to widespread adoption. Latter using the example of the EU Digital Identity Wallet (EUDIW) and the recent discussion and feedback from cryptography experts around this topic. We also briefly comment on the transition to post-quantum cryptography.
This study introduces a secure and scalable hybrid trust management system for Vehicular Ad Hoc Networks (VANETs), designed to detect and mitigate diverse malicious activities, including Sybil attacks, Denial of Service (DoS), and data forgery. Integrating entropy-based anomaly detection, Bayesian reputation updating, and the IOTA Tangle as a distributed ledger, the system ensures robust, real-time trust evaluation in dynamic vehicular environments. Rigorous testing against adversarial scenarios demonstrated high detection accuracy, low latency, and resilience under varied attack conditions. Key contributions include enhanced scalability through edge computing, secure reputation storage using Distributed Ledger Technology (DLT), and real-time anomaly detection.
Federated Learning (FL) transformed decentralized machine learning by allowing joint model training without mutually sharing raw data, hence being especially useful in privacy-sensitive applications like healthcare, e-commerce, and finance. Even with its privacy-focused architecture, FL is vulnerable to a range of security attacks such as data poisoning, model inversion, membership inference attacks, and communication interception. These attacks compromise the confidentiality of patients in healthcare, consumer data privacy in e-commerce, and financial safety in banking, thus necessitating effective privacy-preserving mechanisms. This survey presents a classification of security threats in FL, grouping them by their source, effect, and attack mode. We review state-of-the-art countermeasures, such as differential privacy, secure multi-party computation, homomorphic encryption, and resilient aggregation methods, their effectiveness, trade-offs, and real-world applicability to FL. In medicine, FL enables joint disease diagnosis without compromising patient confidentiality; in online shopping, it provides personalized suggestions without revealing customer tastes; and in banking, it improves fraud detection without violating regulatory requirements. In addition, we discuss future horizons in privacy-preserving FL, including adversarial robustness, blockchain-protected models, and tailored FL architectures, improving security and resiliency in these domains. We also discuss the balancing problems between security, accuracy, and computational efficiency with possible trade-offs in scaling privacy-preserving FL By analyzing threats and mitigation strategies systematically, this paper will provide direction to future research on designing secure, scalable, and privacy-preserving FL frameworks for the changing healthcare, e-commerce, and finance needs.
Open access
Privacy-Preserving Technologies in Data
Cryptography and Data Security
Artificial Intelligence in Healthcare and Education
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.
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.
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.