Blockchain has become a cornerstone of trustworthy, decentralised information governance. Consensus protocols and cryptographic linkages guarantee data integrity, immutability, and verifiable provenance, eliminating reliance on a single trusted authority and mitigating data fragmentation. Within smartâhealthcare ecosystems, these capabilities enable the shift from siloed, centralised repositories to distributed, patientâcentric infrastructures. Because clinical data are highly sensitive and strictly regulated, robust assurances of integrity, confidentiality, and fineâgrained authorisation are essential. Integrating blockchain and smart contracts with technologies such as distributed offâchain storage and the Internet of Medical Things (IoMT) creates a resilient, scalable, and interoperable foundation for nextâgeneration healthcare data management. This research introduces hChain, a fourâgeneration family of distributedâledger frameworks that progressively strengthen security, intelligence, and scalability in smartâhealthcare environments. hChain 1.0 lays the groundwork with a blockchain architecture that safeguards patient data, supports realâtime clinical telemetry, and enables seamless interâinstitutional exchange. Building on this foundation, hChain 2.0 integrates InterPlanetary File System (IPFS) storage and smartâcontract enforcement to deliver tamperâproof, fineâgrained access control. hChain 3.0 embeds onâchain deepâlearning analytics, providing proactive, automated decision support across the care continuum while preserving data integrity. Finally, hChain 4.0 introduces a highly scalable, permissioned ledger augmented by an AttributeâBased Access Control (ABAC) layer, ensuring dynamic, contextâaware authorisation in complex organisational settings. The results demonstrate practical solutions for transforming data infrastructures from centralised to decentralised architectures, providing techniques that facilitate seamless integration with existing systems while enhancing blockchain scalability and privacy.
In this framework, Blockchain-Integrated Access Control for Wireless Edge Networks intends to attempt authentication and authorization by using smart contracts and immutable ledgers making it secure and decentralized. It increases trust among edge nodes by connecting them, thereby creating a single point of failure, while providing transparent and tamper-resistant enforcement of policies, which improves scalability, resilience, and performance, ultimately making it the Mold for IoT and edge computing environments. The objectives that the system intends to apply towards are design and implement decentralized access control for wireless edge networks using Blockchain, to provide tamper-proof identity verification solutions, to ensure dynamic access policies enforced through smart contracts, to reduce dependency on central authorities, and also to increase security and privacy, scaling trust, and transparency in the distributed IoT and edge environments. The proposed system proposed to implement decentralized access control via private Blockchain in wireless edge networks. Smart contracts are crafted to dynamically facilitate identity authentication, access rights, and the enforcement of policies. Edge nodes interface with the Blockchain to verify credentials and log access attempts immutably. To curb latency and overhead, lightweight cryptography schemes and consensus algorithms such as PBFT are employed. Simulation in a wireless edge environment showed improvements in access request validation by 35%, unauthorized access attempts down by 42%, and improved scalability with respect to conventional centralized models, showing that the model is effectual and robust in secure access control.
The demand for privacy-preserving machine learning has led to the rise of Federated Learning (FL), where multiple clients collaboratively train a model without sharing raw data. Despite its privacy benefits, FL is vulnerable to Byzantine failures, where malicious or faulty participants inject corrupted updates, threatening model integrity. To address this, a range of Byzantine-resilient aggregation techniques have been proposed, including statistical filters (e.g., Trimmed Mean, Krum), trust-based weighting, cryptographic protocols, and hybrid strategies. This paper presents a systematic literature review (SLR) of these defenses, evaluating their robustness, scalability, and suitability for real-world applications. Challenges such as non-IID data, adaptive attacks, and trade-offs between security and efficiency are critically examined. In addition, we explore emerging trends such as domain-specific defenses, energy-aware FL, quantum-resilient methods, and federated zero-knowledge proofs. A novel classification of hybrid approaches and a standardized benchmarking framework are proposed to guide future research. This review aims to support the development of resilient, efficient and scalable decentralized learning systems in adversarial environments.
Blockchain technology presents transformative opportunities for secure personal data sharing, particularly in healthcare, finance, and identity management. However, its widespread adoption is constrained by challenges such as limited scalability, privacy concerns, and conflicts with regulatory frameworks like the General Data Protection Regulation (GDPR). This study introduces a novel hybrid framework that integrates the InterPlanetary File System (IPFS) for off-chain storage with Zero-Knowledge Proofs (ZKPs) to enhance privacy, ensure regulatory compliance, and reduce on-chain storage demands. Employing a Design Science Research (DSR) methodology, the framework was developed and validated using Ethereum and Hyperledger Fabric, guided by insights from a systematic review of 180 studies from 2018 to 2023. Empirical evaluations revealed a 75% reduction in blockchain storage, 98% GDPR compliance, and zk-SNARK proof verification times below one second. The framework also enables GDPR-compliant erasure by removing encrypted off-chain data while preserving on-chain auditability. Despite challenges such as IPFS latency and trusted setup complexities, the solution offers a scalable and privacy-preserving architecture applicable to real-world domains, especially in privacy-critical environments like healthcare and finance by resolving blockchainâs GDPR compliance paradox.
Syed Abrar Ahmed, Ricardo Correia, Anderson Oliveira do Carmo, Henrique Martins
Increasingly, across geographies, citizens are requiring access to and control of their health data. This paper examines the "Logging Component" proposed by the European Health Data Space (EHDS) regulation and its crucial role in facilitating secure and transparent access to electronic health records (EHR) and health data. We analysed the proposal for the five elements of the Logging Component (LC): identification of data accessors, identification of data subjects, categorisation of accessed data, temporal logging, and data origin tracking. Explored how these elements contribute towards enhanced accountability and compliance in health data management. We experimented with distributed ledger technology (DLT) to support the "data origin tracking element", reaching the demonstration level which can be presented. We used hybrid DLT to develop a system for immutable storage of access logs, and for using smart contracts to maintain a self-governing decentralised access control list (ACL) directly integrated with EHR and PHR systems. We found that the LC is more than a regulatory requirement. It can serve as a framework for the integration of advanced technologies, e.g. DLT and others, increasingly mature and potentially foundational to building new networks of trust among stakeholders, while ensuring data privacy in cross-border and intra-border healthcare scenarios. The study also identified shortcomings of the LC, such as the absence of "purpose logging", which was conceptualised and proposed. This study contributes to the understanding of how logging mechanisms can enhance transparency and accountability in electronic health record systems within the European healthcare landscape, but with potential usefulness for the "Global EHR". In conclusion, our findings suggest that the successful implementation of the five elements of the Logging Component are mandatory and can benefit from mature advance technologies, but the sixth element proposed by us would be critical for achieving the EHDS's broader objectives of harmonised health data sharing in Europe and beyond while maintaining robust security standards.
Advancements in personalized medicine require secure, transparent, and privacy-preserving genomic data management systems. This study proposes a novel hybrid blockchain-based genomic data model integrating Self-Sovereign Identity (SSI), Decentralized Identifiers (DIDs), Verifiable Credentials (VCs), and decentralized storage Interplanetary File System (IPFS) to enable secure, privacy-compliant, patient-controlled genomic data exchange. The model leverages Polygon Proof-of-Stake (PoS) blockchain by deploying a smart contract that enforces fully access control functions applied on 100 samples of synthetic genomic data, ensuring only authorized researchers with valid DIDs and VCs can retrieve genomic data. Later, we performed five tests to evaluate our model performance. Security evaluations confirmed 100% data integrity validation through SHA-256 hash validation on-chain, ensuring tamper-proof genomic data storage. Unauthorized access attempts resulted in zero successful breaches, demonstrating the robustness of SSI-based authentication by showing revoked access. Additionally, IPFS data availability testing confirmed reliable and decentralized data retrieval through the Content Identifier (CID) on-chain. The model's access revocation mechanism enabled real-time patient control over genomic data access, ensuring compliance with GDPR privacy regulations. The proposed model provides a scalable, secure, and privacy-compliant solution for genomic data sharing in precision medicine, empowering patients with full control over their genetic data while facilitating researchers to trustworthy, decentralized data useability for precision research.
Human genetic data, crucial for advancing personalized medicine, requires secure and privacy-preserving management solutions. Traditional approaches face challenges in scalability, security, and decentralized access control. This study proposes a blockchain-based framework leveraging Thirdweb and Ethereum smart contracts to address these issues. The framework integrates decentralized storage via IPFS for cost-efficient off-chain genetic data storage, while on-chain smart contracts manage access control, encryption, and audit trails. Utilizing Solidity for smart contract development, the system ensures role-based permissions, wallet-based authentication, and immutable transaction logging. Genetic data in FASTA format, sourced from NCBI, is encrypted and linked to IPFS hashes stored on the blockchain. The architecture supports dual interfacesâcommand-line for developers and a Thirdweb dashboard for end-usersâenabling secure data upload, access, and monitoring. Testing demonstrated functional efficacy in data integrity, access verification, and audit capabilities. Results highlight the systemâs ability to enhance privacy, eliminate intermediaries, and provide transparent data governance. The integration of Thirdweb further decentralizes operations, aligning with Web 3.0 principles. Key contributions include a scalable model for genetic data sharing, a customizable smart contract template, and a user-centric design. Future work should explore advanced encryption, real-world healthcare integration, and performance optimization under high-throughput conditions. This research bridges biotechnology and blockchain, offering a robust foundation for secure genomic data ecosystems.
The increasing adoption of tokenized assets, Decentralized Finance (DeFi) applications, and the exploration of Central Bank Digital Currencies (CBDCs) necessitate sophisticated security architectures for Regulated Tokenized Multi-asset Networks (RTMNs). This paper addresses the complex interplay between privacy, and composability within these emerging decentralized financial ecosystems. It is argued that conventional security paradigms, predominantly reliant on perimeter defenses, are insufficient for the distributed and interconnected nature of DeFi infrastructures. While Zero Trust models offer relevant principles, their direct application within regulated, high-performance financial networks, particularly those involving CBDCs or complex DeFi protocols, presents significant challenges regarding compliance and efficiency. This paper introduces a novel framework meticulously designed to support diverse RTMN use cases, including retail/wholesale CBDC, tokenized deposit, stablecoin and multi-asset platforms operating within a DeFi context. A foundational element of this framework is the implementation of cryptographically enforced information compartmentalization. This ensures that each architectural component operates with the minimum necessary information required for its specific function, inherently embedding privacy-by-design and preventing unauthorized access to comprehensive network or transactional data. The proposed framework is architected to guarantee critical properties essential for robust distributed and decentralized systems: (1) Atomicity of transactions; (2) Composability and Programmability; (3) Settlement Finality, providing transaction immutability; (4) Enhanced Privacy and Security, leveraging cryptographic techniques; (5) Support for Distribution and Decentralization; (6) Performance, addressing throughput and latency demands; and (7) Continuous monitoring and auditing, enabling regulatory oversight without compromising user data. It is provided a detailed analysis of the framework's application across distinct RTMN implementations, identifying specific technical challenges and opportunities within the context of tokenized systems. Furthermore, the paper presents a qualitative and functional evaluation of the framework's characteristics applied to the use case of tokenizing Federal Government Securities in an RTMN, such as Drex. Fundamentally, the inherent trade-offs between cryptographic privacy guarantees, compartmentalization, and programmability are examined, exploring optimization strategies relevant to demanding DeFi and institutional applications, such as decentralized trade finance and tokenized debt instruments.
Federated Learning (FL) offers an attractive framework for collaboratively training AI models while preserving data privacy. However, it also introduces challenges in verifying the integrity and authenticity of model updates across diverse clients. Zero-Knowledge Proofs (ZKP) provide a promising means to address these issues by verifying computations without revealing underlying data. Yet, global verification using ZKP remains computationally expensive and does not scale well. To overcome these limitations, we propose a novel approach grounded in two key principles: (a) partial verification, targeting carefully selected subsets of data, can effectively mitigate adversarial attacks; and (b) robust data verification is essential, ensuring not only the consistency of model parameters but also the authenticity of the underlying data. We highlight the potential operation of this partial verification system, discuss novel research directions, and outline strategies for a wider integration into FL architectures.
As governmental health information systems become increasingly digitized, the need for collaborative analytics across decentralized regions has intensified.However, privacy concerns, regulatory constraints, and infrastructure disparities have limited the extent to which sensitive health data can be aggregated and analyzed across jurisdictions.This paper explores the integration of blockchain technology with federated learning (FL) to enable privacy-preserving data analytics across distributed governmental health information systems.By combining FL's decentralized model training capabilities with blockchain's immutable, transparent ledger and consensus mechanisms, the proposed framework ensures secure, auditable, and policy-compliant data collaboration without requiring raw data exchange.The framework leverages smart contracts to automate access control, consensus validation, and compliance enforcement among participating health institutions.Each node (representing a governmental health entity) trains models locally and shares only encrypted model parameters, which are validated and recorded on the blockchain.This eliminates the need for centralized authorities and reduces the risk of data leakage or manipulation.A core contribution of this work lies in addressing public-sector constraints such as legacy infrastructure, heterogeneous data standards, and institutional trust gaps through a modular, interoperable design.The system includes support for dynamic node participation, real-time updates, and compatibility with health data standards such as HL7 and FHIR.Use-case simulations across municipal, regional, and national health departments demonstrate improved efficiency in outbreak prediction, chronic disease surveillance, and population-level risk stratification while maintaining strict compliance with data protection regulations.This paper advances a scalable and trustworthy architecture for cross-border health collaboration, offering a blueprint for digital public health infrastructures in the age of data sovereignty and distributed intelligence.
The rapid evolution of phishing attacks targeting email, chat, and social media platforms poses a significant threat to digital security, with a reported 667% surge in spear-phishing during the 2020 COVID-19 crisis [1]. Current AI-based detection systems face challenges in dataset diversity, adversarial robustness, computational scalability, model interpretability, and privacy preservation, limiting their efficacy in real-time, multi-platform environments. This paper introduces PhishGuard, an innovative framework for real-time phishing detection, designed to overcome these limitations. PhishGuard integrates lightweight transformer models (e.g., distilled BERT), hybrid detection techniques combining natural language processing (NLP), propagation analysis, and user behavior analysis, and explainable AI (XAI) methods like SHAP and LIME for transparent decision-making. Privacy-preserving techniques, including federated learning and local differential privacy, ensure secure processing of sensitive user data. Evaluated on diverse datasets such as PhiKitA, Enron, and a custom social media corpus, PhishGuard achieves up to 97.5% accuracy, 94% F1-score, and inference times below 5 ms, demonstrating scalability for resource-constrained devices. The framework also incorporates zero-knowledge proofs for verifiable inference, addressing trust and integrity concerns. By tackling cross-domain generalization, adversarial robustness, and real-time performance, PhishGuard offers a scalable, user centric solution for secure digital communications, with applications in finance, healthcare, and social media platforms. Future enhancements include multilingual support and image based phishing detection, paving the way for a comprehensive defense against evolving cyber threats.
In the domain of authentication, information leakage which can lead to identity theft represents a significant challenge in the field of cybersecurity. This challenge is particularly relevant in the context of 5 G tactical bubbles, where secure and efficient authentication mechanisms are critical to gain access to sensitive information and communication services. The concept of Zero-Knowledge Proofs, in particular non-interactive proofs, has gained attention in recent years as robust cryptographic methods for privacy-preserving protocols. Zero-knowledge proofs enable users to prove possession of specific knowledge to verifiers without revealing the knowledge itself in a single interaction round. Despite its growing popularity, Zero-Knowledge Proofs have not yet been fully explored within 5 G tactical bubbles. In this paper, we perform a comparative analysis between traditional authentication mechanisms and Zero-Knowledge Proofs-enabled authentications. To this end, we evaluate the feasibility in terms of time and computational complexity and determine whether these advanced authentication protocols can ensure enhanced privacy and security in 5 G tactical bubbles.
Web3 applications, such as on-chain games, NFT minting, and leader elections necessitate access to unbiased, unpredictable, and publicly verifiable randomness. Despite its broad use cases and huge demand, there is a notable absence of comprehensive treatments of on-chain verifiable randomness services. To bridge this, we offer an extensive formal analysis of on-chain verifiable randomness services. We present the first formalization of on-chain verifiable randomness in the blockchain setting by introducing the notion of Verifiable Randomness as a Service (VRaaS). We formally define VRaaS using an ideal functionality$\mathcal{F}\text{VRaaS}$in the Universal Composability model. Our definition not only captures the core features of randomness services, such as unbiasability, unpredictability, and public verifiability, but also accounts for many other crucial nuances pertaining to different entities involved, such as smart contracts. Within our framework we study a generic design of Verifiable Random Function (VRF)-based randomness service - where the randomness requester provides an input on which the randomness is evaluated as VRF output. We show that it does satisfy our formal VRaaS definition. Furthermore, we show that the generic protocol captures many real-world randomness services like Chainlink VRF and Supra dVRF. Moreover, we investigate the minimalism of the frame-work. Towards that, first we show that, the two transactions in-built in our framework are actually necessary for any randomness service to support the essential qualities. We also discover practical vulnerabilities in other designs such as Algorand beacon, Pyth VRF and Band VRF, captured within our framework.
The European Union's (EU) data strategy aims to create a single market for seamless data flow while ensuring proper governance, privacy, and data protection. In this paper, we present SEDIMARK, an EU project, that builds on this strategy by developing a fully decentralised, secure data marketplace. The goal of SEDIMARK is to build a complete toolbox that enables users to purchase and process data assets. The toolbox includes tools for data cleaning, decentralised machine learning models and secure data exchange. SEDIMARK offers users full control over data assets by enabling them to keep their data locally and thus removing the need for central servers. With customisable pipelines and tools, SEDIMARK supports a wide range of users, from novices to experts, promoting seamless collaboration and fair access to high-quality datasets across Europe. The decentralised connectivity in SEDIMARK is achieved with the use of Distributed Ledger Technology (DLT). Furthermore, SEDIMARK's architecture features a unique Connector component using Self Sovereign Identities (SSI), fostering trust and secure interactions. Transactions in SEDIMARK are stored in a Registry, a decentralised, immutable, non-repudiable and permissionless database. Together the technologies used in SEDIMARK ensure privacy, trust and data quality for secure management, sharing, and monetisation of assets in data spaces.
We present a secure and efficient federated learning protocol for autonomous vehicles that resists data leaks, redundancy, and adversarial attacks. Our system combines fast zero-knowledge proofs and compressed Bloom filters to verify updates without exposing private data. Compared to traditional approaches, our method reduces proof sizes by 90 % (under 10 KB), memory by up to 75 %, and maintains accuracy with less than 4% degradation under 30% attack rates. The entire update cycle completes in under 600 ms, making it practical for real-time use in vehicles. This work advances trustworthy AI deployment in dynamic, resource-limited networks.
Wael Issa, Nour Moustafa, Benjamin Turnbull, KimâKwang Raymond Choo
Sixth-generation (6G) wireless networks are set to transform the Internet of Things (IoT) by enabling faster, smarter, and more connected systems. These networks will bring together a wide range of devices, including cars, robots, industrial machines, and smartphones, to support edge intelligence and real-time decision-making. Federated learning (FL) supports this shift by allowing devices to collaboratively train models without sharing raw data, which helps to protect user privacy. Despite its advantages, FL faces significant security challenges, including poisoning attacks and Byzantine clients, both of which can compromise the training process and degrade the accuracy and reliability of the global model. Although existing methods can detect malicious updates, many advanced attacks still bypass statistical defenses relying on metrics such as median and distance. Thus, developing an FL system that ensures both reliable decision-making and privacy and security guarantees in IoT networks remains a significant challenge. This study introduces a Digital Twin-driven Blockchain-enabled Federated Learning (DT-BFL) framework designed for IoT networks. The framework creates a digital representation of the IoT environment to support secure and decentralized edge intelligence using blockchain and federated learning technologies. DT-BFL is built to detect and filter out potentially poisoned model updates from malicious participants. This is achieved through a new smart contract-enabled decentralized aggregation method called Local Updates Purify (LUP). LUP uses a two-stage filtering process: First, it applies Median Absolute Deviation (MAD) to initially remove outliers, then uses statistical features and clustering to separate honest from malicious updates before aggregating the global model. It also assigns a Trust Score (TS) to each participant based on how much their updates differ from the global model and then uses a genuine criterion to select honest clients by evaluating trust scores, update similarity, and deviation from the global model. Experimental results show that DT-BFL effectively defends against various poisoning attacks on datasets like MNIST, ToN-IoT, and CIFAR-10 using models such as CNN, MLP, ResNet, and DenseNet, and maintains high accuracy even when 50% of the clients are malicious. Using a permissioned blockchain further secures the system by enabling aggregation of the decentralized model and authentication of clients through smart contracts. The source code is available on https://github.com/UNSW-Canberra-2023/LUP .
The rise of Industry 5.0 focuses on merging advanced intelligence, automation, and human-centered teamwork in industrial settings. However, keeping interconnected IoT networks secure is still a challenging problem. This paper proposes a new security framework that combines Blockchain, Federated Transfer Learning, and zero trust network (ZTN) principles to improve IoT security in Industry 5.0. Blockchain is a decentralized ledger that ensures secure data sharing and protects model updates. Federated Transfer Learning allows model training across distributed IoT devices to keep data private. The ZTN approach enforces strict access rules, assuming that no entity is trusted by default. The proposed framework offers a scalable and resilient solution to protect next-generation industrial IoT networks, using Blockchain for data security, transfer learning for adaptability, and ZTN for strict access control. The ZTN architecture strengthens security by checking every access request and keeping the IoT system safe. The experimental results show good performance of the proposed method, with better accuracy, precision, recall, and F1 scores. The model achieved an accuracy of 0.85, 0.88, and 0.87 for learning rates of 0.01, 0.001, and 0.0001, respectively, at 100 epochs. The precision values reached 0.84, 0.87, and 0.86, while the recall scores were 0.82, 0.86, and 0.85, respectively. The F1-scores were recorded at 0.83, 0.86, and 0.85, which confirms the robustness of our model.
Ahmed Sayed M. Metwally, Yazeed Alhumaidan, Saad Alzahrani, Mohamed H. Abdelati
Implementing artificial intelligence (AI) and blockchain technology in management systems transforms traditional libraries into advanced information centers that are data-driven and effectively managed. While these technologies enhance efficiency and operational capabilities, they also present two critical challenges: data privacy and ethical concerns. This study examines the role of AI and blockchain in library management, focusing on issues related to data privacy and ethical challenges that arise from their use. It also offers best practices to ensure safe implementation. The research adopts a comprehensive mixed-methods approach, involving qualitative interviews and quantitative surveys to identify these challenges within the system architecture, assess the effectiveness of current designs, and propose a complete framework using privacy-preserving technologies. This framework incorporates innovative cryptographic techniques, including homomorphic encryption, differential privacy, and zero-knowledge proofs, providing a novel model for the ethical use of AI in libraries. The findings indicate that robust data protection, transparency, and accountability are essential to building trust in AI-powered library services.
Zhaowei Hu, Jin Rui-fang, HangYi Quan, Shiyun Ni · 5 authors
To address privacy leakage risks arising from low collaborative user engagement, third-party trust deficits, and insufficient collaboration timeliness in location-based services (LBS), this paper proposes a dual-protection framework integrating blockchain technology and threshold cryptography for safeguarding location privacy. The framework employs asymmetric encryption with Shamir's (t, n) secret sharing to encrypt user queries, distributing decryption key fragments to collaborative users while generating n anonymous service requests through location generalization strategies. A temporary private blockchain constructed using smart contracts ensures confidential data transmission, supported by a dynamic privacy parameter configuration system based on Byzantine fault tolerance. The framework implements a priority-response consensus mechanism through Token-based equity proof-of-stake, prioritizing service for users with higher Token values. To mitigate privacy breaches caused by unresponsive collaborators, a competitive incentive mechanism ensures timely information submission. Through ciphertext fragment verification algorithms and Lagrange interpolation-based key reconstruction, the framework enables secure query decryption and service matching in untrusted third-party environments, guaranteeing information security, integrity, and non-repudiation. Experimental validation using real-world datasets confirms the framework's feasibility and operational effectiveness.
Ensuring privacy and security in healthcare data sharing is critical due to the sensitive nature of patient information and the growing threat of cyber attacks. This paper explores the development of privacy-preserving data-sharing protocols for healthcare systems by integrating cryptographic techniques and blockchain technology. The study aims to establish a secure framework that facilitates seamless data exchange among healthcare stakeholders while maintaining data integrity, confidentiality, and access control. Key cryptographic mechanisms, including homomorphic encryption, zero-knowledge proofs, and attribute-based encryption, are employed to ensure that only authorized entities can access patient records without exposing sensitive details. Blockchain technology is leveraged to create a decentralized and tamper-resistant ledger, ensuring transparency and auditability in data-sharing transactions. Smart contracts are utilized to enforce predefined access policies automatically, enhancing security and compliance with regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). The findings indicate that the proposed framework significantly mitigates risks associated with unauthorized access, data breaches, and single points of failure. Comparative analysis with traditional centralized systems demonstrates improved efficiency, scalability, and security in healthcare data management. The integration of blockchain and cryptographic techniques ensures robust privacy-preserving mechanisms without compromising accessibility or interoperability. This research provides a novel approach to secure data sharing in healthcare, fostering trust among stakeholders while ensuring compliance with privacy regulations. Future work will focus on optimizing computational efficiency and addressing scalability challenges to facilitate widespread adoption in real-world healthcare ecosystems.
Daria Schumm, Cedric von Rauscher, Katharina Olga Emilia MĂŒller, Burkhard Stiller
Transparency and immutability of blockchains can expose metadata and raise concerns about its classification as personal data under privacy regulations. This paper investigates privacy risks associated with metadata in blockchain-based identity systems. Additionally, two privacy-preserving mechanism designs, namely Zero-Knowledge Proof (ZKP) and Homomorphic Encryption (HE), to protect metadata are proposed. As a result, this work introduces the first use case of HE privacy-preserving mechanism in the context of Decentralized Identity (DI) and Self-Sovereign Identity (SSI) systems.
As global energy markets undergo digitization and decentralization, multinational finance consortia increasingly rely on artificial intelligence (AI) to analyze geospatial and sensor data for investment modeling, risk assessment, and infrastructure optimization.However, cross-border data exchange poses significant privacy, security, and sovereignty concerns-particularly in energy-sensitive contexts where geospatial telemetry and environmental sensor networks contain critical operational intelligence.This paper advances a secure federated learning (FL) architecture tailored for multinational energy finance consortia, leveraging encrypted, AI-driven analytics to harmonize data utility and confidentiality.The proposed architecture integrates homomorphic encryption, differential privacy, and secure multi-party computation within a federated learning framework.It enables collaborative AI model training across sovereign entities and private stakeholders without transferring raw data, thus preserving jurisdictional control while enabling unified forecasting of energy supply, climate impact, and financial risk metrics.The paper details a tiered security model that accommodates variable data sensitivity levels-from satellite imagery and wind turbine telemetry to emission sensors and power grid diagnostics.Furthermore, the study presents a pipeline that processes heterogeneous datasets-such as LIDAR scans, thermal signatures, and remote sensor logs-using encrypted deep learning models capable of geospatial segmentation, anomaly detection, and predictive trend inference.Emphasis is placed on maintaining model accuracy in non-IID (non-independent and identically distributed) data scenarios, a common feature in distributed energy infrastructure.Policy implications are explored through case studies involving regional green bond issuance, multinational solar grid financing, and climate-resilience investments.The paper concludes with a governance blueprint for secure AI collaboration in energy finance ecosystems, balancing transparency, performance, and regulatory compliance.
Federated learning (FL) is increasingly recognised for addressing security and privacy concerns in traditional cloud-centric machine learning (ML), particularly within personalised health monitoring such as wearable devices. By enabling global model training through localised policies, FL allows resource-constrained wearables to operate independently. However, conventional first-order FL approaches face several challenges in personalised model training due to the heterogeneous non-independent and identically distributed (non-iid) data by each individual's unique physiology and usage patterns. Recently, second-order FL approaches maintain the stability and consistency of non-iid datasets while improving personalised model training. This study proposes and develops a verifiable and auditable optimised second-order FL framework BFEL (blockchain enhanced federated edge learning) based on optimised FedCurv for personalised healthcare systems. FedCurv incorporates information about the importance of each parameter to each client's task (through fisher information matrix) which helps to preserve client-specific knowledge and reduce model drift during aggregation. Moreover, it minimizes communication rounds required to achieve a target precision convergence for each client device while effectively managing personalised training on non-iid and heterogeneous data. The incorporation of ethereum-based model aggregation ensures trust, verifiability, and auditability while public key encryption enhances privacy and security. Experimental results of federated CNNs and MLPs utilizing mnist, cifar-10, and PathMnist demonstrate framework's high efficiency, scalability, suitability for edge deployment on wearables, and significant reduction in communication cost.
The rapid adoption of Federated Learning (FL) in privacy-sensitive domains such as healthcare, IoT, and smart cities underscores its potential to enable collaborative machine learning without compromising data ownership. However, conventional FL frameworks face several critical challenges: high computational overhead on edge devices, significant communication latency due to frequent model updates, vulnerability to model and data poisoning attacks, and limited privacy-preserving mechanisms that expose systems to inference risks. These issues hinder the scalability, efficiency, and trustworthiness of FL in real-world, large-scale deployments-particularly in domains like Electronic Health Records (EHR) management, where data sensitivity is paramount. To address these challenges, this paper introduces the Enhanced Privacy-Preserving Blockchain-Enabled Federated Learning (EPP-BCFL) framework, which integrates blockchain with hybrid privacy mechanisms and intelligent aggregation strategies. The architecture comprises three layers: (1) an Edge Nodes Layer for on-device learning; (2) a Federated Aggregation Layer using Secure Multi-Party Computation (SMPC) and Differential Privacy (DP); and (3) a Blockchain Layer with a lightweight PoS + BFT consensus mechanism. Experimental evaluation on CIFAR-10 demonstrates 95.2% accuracy, a 43% reduction in communication latency, a 37% decrease in computational cost, and robust defense against data/model poisoning and adversarial attacks. Attack resilience improved accuracy from 72.5 to 93.2%, while privacy budget tuning achieved 90.3% accuracy at Δ = 1.0. Compared to state-of-the-art models, EPP-BCFL exhibits superior performance in terms of security, scalability, and support for edge device heterogeneity, validating its applicability in secure EHR management.