S. Edwin Raja, K Dinesh Kumar, K. Manikandan, P Senthil · 6 authors
Blockchain adds more value to healthcare since its tools emerge as an increasingly popular way to secure, transparently, and tamper-proof sensitive medical records. Nevertheless, current consensus algorithms (i.e., Proof of Work (PoW), Proof of Stake (PoS), and Practical Byzantine Fault Tolerance (PBFT)) use a lot of energy, have latency issues and are not scalable enough or clinically context-aware, which makes them in applicable to healthcare settings. Due to the lack of efficiency of existing solutions, a new lightweight consensus algorithm dedicated to decentralized healthcare systems is proposed, MediCon, in this paper. MediCon incorporates domain-sensitive capabilities, including a Reputation Score (R) where candidates are punished based on their behavior as validators, an Urgency Index (U) that places making progress in clinical transactions in priority, a Committee Rotation Interval (CRI) that maintains dilution of the committee, and a Weighted Voting (WV) system that combines urgency with institutional credibility. Architecture can be summarized as Patient Data Nodes, an Access Layer that is used to provide consent and emergency overrides, Consensus Nodes that are chosen through trust metrics, and a MediCon Core that is used to parse transaction validation. Transactions are put in order of urgency and sensitivity, and then the most reputable validator proposes them. The last block is confirmed with weighted voting and signed with dedicated healthcare compliant standards (e.g., HIPAA, FHIR). Experimental evidence on a simulated multi-institutional healthcare network shows that MediCon leads to latency, throughput, and energy consumption by 48%, 35%, and 60%, respectively, compared to the current consensus models. The similarities in the logic of consensus and clinical priority, as well as trust and patient consent, MediCon provides scalable and secure basis of next-generation healthcare blockchain systems.
Traditional centralized scholarship evaluation processes typically require students to submit detailed academic records and qualification information, which exposes them to risks of data leakage and misuse, making it difficult to simultaneously ensure privacy protection and transparent auditability. To address these challenges, this paper proposes a scholarship evaluation system based on Decentralized Identity (DID) and Zero-Knowledge Proofs (ZKP). The system aggregates multidimensional ZKPs off-chain, and smart contracts verify compliance with evaluation criteria without revealing raw scores or computational details. Experimental results demonstrate that the proposed solution not only automates the evaluation efficiently but also maximally preserves student privacy and data integrity, offering a practical and trustworthy technical paradigm for higher education scholarship programs.
The objective of this study is to define a compliance-first, conceptually generalisable architecture for a multi-agent artificial intelligence platform integrated with distributed ledger technology, designed to be domain-, deployment-, and vendor-agnostic. It addresses a persistent shortcoming in current AI deployments, where compliance is often treated as a secondary concern, applied retroactively through prompt engineering rather than embedded within the foundational design. The proposed model encodes regulatory, governance, and ESG requirements into an objective-under-constraints framework, ensuring that all specialised agents operate within legally admissible and verifiably auditable parameters prior to any domain-specific implementation. A DAG-based verification layer is incorporated to enable scalable, low-latency, and cost-efficient operation while preserving evidentiary integrity. The analysis evaluates the feasibility of this conceptual model to support sustainable, rapid-deployment vertical applications without inducing vendor lock-in, preserving operational neutrality, and ensuring environmental accountability. The findings suggest that integrating compliance, ESG metrics, and agent specialisation at the architectural level provides a transferable foundation for cross-domain AI-DLT infrastructures.
The adoption of smart home technologies has increased and that it is needed to have robust and fine-grained access control mechanism to protect on the sensitive data and stop drop privacy. In this research, we propose new framework mining blockchain technology, smart contracts that enforce granular access control in smart home environments based on biometric smart contracts. Blockchain's immutable ledger, together with the characteristics of its decentralized architecture, make blockchain a secure storage for access logs, using biometric confirmation to further identify the owner of the account and minimize the risks of the unauthorized accesses. Access policies are dynamically managed and validated by smart contracts, which allows it to make real time decision. The proposed system is experimentally evaluated and the proposed system reliably improves security with just a 96.2% success rate of enforcing access control latency less than 1.5 seconds. One of the future works will be to scale the system and incorporate adaptive learning model to adjust the access policies.
The secure management of Electronic Health Records (EHRs) in a cloud environment poses many challenges, and guaranteeing the scalability of a secure solution to manage the huge amounts of data and its privacy remains an open problem. Although the existing encryption methods offer strong security, the widespread adoption of asymmetric-key protocols is limited because of the lack of computational efficiency compared with practical applications. That is, the efficiency of computational time required for an encryption or decryption step, and the privacy-preserving verification of the computation result, are not balanced due to the volume of the data. In order to overcome the drawbacks of existing encryption methods, we propose a novel approach to integrating Zero-Knowledge Proofs (ZKPs) with a Multi-Layer Merkle Tree (MLMT) to achieve a scalable, privacy-preserving, guarantee-of-integrity and publicly-verifiable solution for managing the EHR while ensuring patients’ privacy. It proposes the utilization of MLMT to build a hierarchical data verification structure for massive data, significantly improving the computational efficiency. It also employs ZKP to enable verifier to verify the validity of data without revealing any record information, which is vital for the data management in the healthcare sector. The authors compared the proposed model with existing approaches which adopt AES-256 encryption and typical Merkle Tree-based solutions and demonstrate its superior scalability and privacy-preserving ability while ensuring controllable computational overhead. The results demonstrate that ML MMT ZKP provides the best balance between privacy, integrity and scalability reaching lower overheads and shorter verification times than other traditional approaches. This work constitutes a step forward in the development of cryptographic solutions for EHRs and provides a framework for real-time verifiable information in the healthcare domain.
The fragmentation of AI agent ecosystems has created urgent demands for interoperability, trust, and economic coordination that current protocols -- including MCP (Hou et al., 2025), A2A (Habler et al., 2025), ACP (Liu et al., 2025), and Cisco's AGP (Edwards, 2025) -- cannot address at scale. We present the Nanda Unified Architecture, a decentralized framework built around three core innovations: fast DID-based agent discovery through distributed registries, semantic agent cards with verifiable credentials and composability profiles, and a dynamic trust layer that integrates behavioral attestations with policy compliance. The system introduces X42/H42 micropayments for economic coordination and MAESTRO, a security framework incorporating Synergetics' patented AgentTalk protocol (US Patent 12,244,584 B1) and secure containerization. Real-world deployments demonstrate 99.9 percent compliance in healthcare applications and substantial monthly transaction volumes with strong privacy guarantees. By unifying MIT's trust research with production deployments from Cisco and Synergetics, we show how cryptographic proofs and policy-as-code transform agents into trust-anchored participants in a decentralized economy (Lakshmanan, 2025; Sha, 2025). The result enables a globally interoperable Internet of Agents where trust becomes the native currency of collaboration across both enterprise and Web3 ecosystems.
Robert Canady, Chandreyee Bhowmick, Xenofon Koutsoukos
Federated learning has become increasingly popular for its ability to process large, distributed datasets and speed up learning while protecting data privacy. However, it typically relies on a central server for coordination, which can be a bottleneck and a single point of failure. To address these limitations, we developed a novel distributed learning architecture that eliminates the need for a central server. The architecture utilizes the hashgraph consensus algorithm (HCA), a distributed ledger technology, which enables the computing nodes to train machine learning models using local data and aggregation with models received from their neighbors. Our work demonstrates that distributed learning using hashgraph consensus can be performed efficiently and is a valid alternative to traditional federated learning. To strengthen this claim, we analyze resilient federated learning in a decentralized setting. Our analysis includes scenarios with denial-of-service and model poisoning attacks. We introduce trimmed soft-medoid (TSM), a resilient aggregation method that has proven resilience to model poisoning attacks. It can be performed at every node using the information available from the hashgraph. An extensive evaluation is conducted using two multimodal machine learning tasks, human emotion recognition and activity recognition. The results confirm that decentralized learning using hashgraph consensus maintains performance parity with traditional federated learning using a central server. This is shown in both normal and adversarial scenarios. We also evaluate the latency and memory overhead of the proposed approach. These are reported to be under an acceptable range, latency of 1s and memory overhead of 8.8-13 GB, for decentralized machine learning.
<p dir="ltr"><b>Advances in Identity and Access Management (IAM): Systematic Insights into AI, Blockchain, and Zero Trust Architectures</b> <p dir="ltr">In an era of expanding digital infrastructure, cloud computing, and remote work, robust Identity and Access Management (IAM) systems are critical for securing sensitive data and ensuring regulatory compliance. This research paper provides a comprehensive systematic review of recent advancements in IAM technologies, addressing the limitations of traditional centralized systems, such as single points of failure and privacy concerns. Utilizing the PRISMA methodology, the study analyzes five peer-reviewed articles from a pool of 23 retrieved from Scopus, published between 2021 and 2025. Key innovations explored include passwordless authentication, AI-driven adaptive authentication, Zero Trust architectures, decentralized identity (DID), self-sovereign identity (SSI), and privacy-enhancing cryptographic techniques like zero-knowledge proofs. The review highlights their applications in multi-cloud, IoT, and hybrid environments, emphasizing enhanced security, user experience, and interoperability. Challenges such as standardization gaps, implementation costs, and privacy concerns are discussed, alongside future directions, including universal protocols and IoT integration. A publicly accessible dataset (DOI: 10.5281/zenodo.12345678) ensures reproducibility. This work serves as an essential resource for cybersecurity researchers and practitioners seeking to navigate the evolving landscape of IAM technologies.
Carlo Segat, Sandro Rodriguez Garzon, Axel Küpper
Self-Sovereign Identity (SSI) is a paradigm for digital identity management that offers privacy and flexibility advantages. A key technology in SSI is Decentralized Identifiers (DIDs) and their associated metadata, DID Documents (DDOs). DDOs contain crucial verification material such as the public keys of the entity identified by the DID (i.e., the DID subject) and are often anchored on a distributed ledger to ensure security and availability. Long-lived DIDs must support updates (e.g., key rotation). Ideally, only the DID subject should authorize DDO updates. However, in practice, update capabilities may be shared or delegated. While the DID specification acknowledges such scenarios, it does not define how updates should be authorized when multiple entities jointly control a DID (i.e., group control). This article examines the implementation of an on-chain, trustless mechanism enabling DID controllers under group control to program their governance rules. The main research question is the following: Can a technical mechanism be developed to orchestrate on-chain group control of a DDO in a ledger-agnostic and adaptable manner?
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 smart‑healthcare ecosystems, these capabilities enable the shift from siloed, centralised repositories to distributed, patient‑centric infrastructures. Because clinical data are highly sensitive and strictly regulated, robust assurances of integrity, confidentiality, and fine‑grained authorisation are essential. Integrating blockchain and smart contracts with technologies such as distributed off‑chain storage and the Internet of Medical Things (IoMT) creates a resilient, scalable, and interoperable foundation for next‑generation healthcare data management. This research introduces hChain, a four‑generation family of distributed‑ledger frameworks that progressively strengthen security, intelligence, and scalability in smart‑healthcare environments. hChain 1.0 lays the groundwork with a blockchain architecture that safeguards patient data, supports real‑time clinical telemetry, and enables seamless inter‑institutional exchange. Building on this foundation, hChain 2.0 integrates InterPlanetary File System (IPFS) storage and smart‑contract enforcement to deliver tamper‑proof, fine‑grained access control. hChain 3.0 embeds on‑chain deep‑learning 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 Attribute‑Based Access Control (ABAC) layer, ensuring dynamic, context‑aware 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.
Dinu-Ştefan Rusu, Emilia-Oana MÎŞŞ, Andrei Vasilățeanu
Ownership of medical data is one of the most important things a person should aim to achieve. An application that allows the patient to be in full control of his data whilst allowing medical personnel to see it such that they can respond accordingly represents a milestone in the development of a smart and integrated emergency system. Such a system should hold the entire patient data, such as prescriptions, vital data and doctor appointments. In this paper we present a system that uses distributed ledger technology to handle medical data such as prescriptions and patient vital data and we showcase how large language models can be used with these technologies to provide a natural interface for the users whilst allowing the user to be in-control of his data. We have also studied how zero knowledge proofs would improve the use case in which a user presents his prescription to a pharmacist.
While there are mechanisms for identifying patients and professionals in most jurisdictions, cross-jurisdictional identification of who accessed health information remains a significant challenge. Initiatives like EHDS, TEHDAS, XpanDH and the European Electronic Health Record Exchange Format (EEHRxF), and xShare have been working to enable cross-border health data usage for healthcare, research, and policy purposes, by proposing and establishing legal, operational and technical foundations. However, the absence of global healthcare professional databases and federated identification and authentication mechanisms perpetuates some challenges for cross-border usage at scale. Based on the work of the EU-funded Blockchain.PT R&D initiative, we propose a distributed ledger technology (DLT)-based architecture and protocol to support a universal EHR system, which combines standardized off-chain storage of sensitive health data with an on-chain layer utilising smart contracts for data certification, access management, and logging of consent and data sharing, processing and transfer agreements. The architecture addresses key requirements of the EHR lifecycle and incorporates emerging decentralised digital identity, authentication and verification protocols to establish the trust layer between the several participants of the healthcare value chain, put citizens in control of their data and partially disintermediate health data exchange and verification. Leveraging the emerging DLT infrastructures such as EBSI/Europeum, and on established digital identity regulations and frameworks such as eIDAS and EUDI, we propose to develop on this EHR-focused architecture and protocol and integrate cross-jurisdictional healthcare professional identification and authentication capabilities. The combination of a universal EHR system with a trusted healthcare professional identity and access management layer could work as a cornerstone for realising the EHDS vision.
The healthcare sector increasingly explores Distributed Ledger Technology (DLT) and Health Web 3.0 Decentralized Applications (DApps) as promising solutions for patient-centric data management, data sovereignty, and privacy-preserving systems. Despite significant research at the intersection of blockchain and healthcare, current efforts predominantly address isolated technical challenges—focusing narrowly on specific mechanisms such as confidentiality, privacy, or individual smart contract vulnerabilities. Even cybersecurity assessments typically examine discrete attack vectors rather than comprehensive threat landscapes. This fragmented approach limits our ability to build trustworthy systems and delays real-world adoption, as stakeholders lack frameworks to holistically evaluate security posture. This study addresses this gap by conducting a comprehensive threat modeling analysis of Health Web 3.0 DApps, taking into account the complex and interconnected security challenges inherent in blockchain-based healthcare systems. We employ a multi-framework approach integrating LINDDUN threat modeling methodology, OWASP Top 10 Smart Contract Vulnerabilities catalog, and Threat Dragon analytical tool to systematically identify, categorize, and evaluate security risks across the entire application stack. Our analysis maps threats spanning smart contract design flaws, cross-chain interaction vulnerabilities, decentralized identity management weaknesses, unauthorized data access risks, and denial-of-service attack vectors. The primary contribution of this work is demonstrating the critical importance and practical value of holistic threat modeling in blockchain healthcare systems. Our findings reveal interdependencies between seemingly isolated vulnerabilities and show how comprehensive security assessment enhances data privacy protection, smart contract integrity, and overall application resilience. This research provides stakeholders with a systematic methodology for deriving trust in blockchain healthcare solutions, advancing both regulatory compliance and user confidence in decentralized medical data management systems.
The works of Garg et al. [S&P’24] (aka hinTS) and Das et al. [CCS’23] introduced the notion of silent threshold signatures (STS) - where a set of signers silently perform local computation to generate a public verification key. To sign a message, any set of t signers sign the message non-interactively and these are aggregated into a constant-sized signature. This paradigm avoids performing expensive Distributed Key Generation procedure for each set of signers while keeping the public verification key constant-sized.In this work, we propose the notion of committee-based silent threshold signature (c-STS) scheme. In a c-STS scheme, a set of signers initially perform a one-time setup to generate the verification key, and then a subset of signers are randomly chosen for an epoch to perform the threshold signing while the other signers are not authorized to sign during that epoch. This captures existing systems like Ethereum Altair and Dfinity where only a specific committee is authorized to sign in a designated epoch. The existing STS schemes cannot be extended to the committee setting because the signature verification only attests to the number of signing parties, not which committee they belong to.So, we upgrade hinTS to the committee setting by proposing Dyna-hinTS. It is the first c-STS scheme and it requires a onetime silent setup and generates a one-time public verification key that does not vary with the committee. Assuming a set of 1024 signers (with corrupt 682 signers), hinTS generates an aggregated signature in 1.7s whereas Dyna-hinTS generates it in 0.35s within a committee of 80 signers. This yields a 4.9× improvement over hinTS for signature generation at the cost of increasing signature verification time by 4% over hinTS. Dyna-hinTS supports general access structure, weighted signatures and improves existing multiverse threshold signatures.
As Internet technology develops swiftly, the significance of privacy protection is escalating in the realms of e-commerce, e-government and software security. Due to the combination of the benefits of proxy signatures and blind signatures, the proxy blind signature scheme not only distributes the workload across application networks but also effectively safeguards the confidentiality of sensitive information. Additionally, the identity-based proxy blind signature protocol can avoid the problem of heavy certificate management and is widely used in electronic commerce and other scenarios. However, some identity-based proxy blind signature protocols that rely on the large integer factorization problem and the discrete logarithm problem are unable to withstand from attacks from quantum computers. Furthermore, current lattice-based proxy blind signature protocols offer only heuristic security and require three rounds of information exchange during the signing phase. In this paper, we introduce a new two-round identity-based proxy blind signature scheme based on lattices. This scheme utilizes a zero-knowledge proof protocol on lattices as its core component to develop an interactive two-round signature scheme that is free from security proof vulnerabilities. Additionally, we validate the security of the proposed protocol within the random oracle model and conduct a performance analysis.
Gang Xu, Shiyuan Xu, Xinyu Fan, Yibo Cao · 7 authors
The Industrial Internet of Things (IIoT) has been widely studied, which dramatically enhanced the manufacturing efficiency and service elasticity. However, how to ensure the data confidentiality and security in the event-driven publish/subscribe communication model becomes a cumbersome problem. To address this concern, ring signatures have been researched deeply. Nevertheless, existing solutions have large computational burdens and neglect to incorporate reporting and tracing features, which makes it impractical for IIoT. In this way, research focus on designing an efficient report and traceable ring signature is still far-reaching. In this article, we propose RAT ring, a novel report and traceable ring signature, which provides publisher authentication, anonymous communication, reporting, and tracing. To achieve this, we adopt the zero knowledge proof to verify the authenticity of publisher data, and the signature of knowledge to trace the signature. Then, we formalize and prove the security of our scheme. Eventually, through comprehensive performance evaluation, our scheme outperforms prior works by approximately up to 51 times in terms of total computational overhead. These results demonstrate that our design is practical and effective for data privacy-preserving in IIoT.
Biagio Boi, Franco Cirillo, Marco De Santis, Christian Esposito
Digital health services for disease diagnosis, followup, and patient empowerment manage data that belongs to a special class of personal information, according to the General Data Protection Regulation (GDPR). For this reason, user authentication and access control are among the key security measures suggested for their protection. However, in the medical context, it is crucial to balance security and privacy support with timeliness and ease of access, which requires innovative solutions. This manuscript introduces an innovative approach leveraging Soulbound Tokens (SBTs) and Zero-Knowledge Proofs (ZKPs), particularly zk-SNARKs, to provide a privacy-aware mechanism for patient authentication in the medical domain. SBTs are utilized within an Attribute-Based Access Control (ABAC) model, ensuring that only eligible patients can access specific medical treatments. In a treatment-specific model, an SBT is issued for each diagnosis, allowing precise control but increasing management complexity. Alternatively, in a diagnosis-categorybased model, SBTs are grouped by diagnostic categories. This reduces the number of tokens and optimizes the space in the patient's wallet but sacrifices some precision in the information. Results demonstrate the timeliness of the proposed approach, with an average time of 6.82s for the release of an SBT and a maximum on-chain verification time of 15.04ms, showcasing their future adoption in a real-time environment, such as the medical context.
R Boopathi, P. Ramakrishnan, M. Jamuna Rani, Pandi Vijayakumar · 6 authors
With Vehicular Ad-Hoc Networks (VANETs) now integrated into intelligent transportation systems, obtaining critical security and trust in such environments where vehicles often exchange sensitive information is becoming a challenge. The paper proposes a new best effort blockchain based trust management framework for the real time data integrity, authentication vehicular identities and malicious behaviors detections. Based on the operating manufacturing environment context, a distributed ledger is applied as a trusted method to immutably store trust scores obtained by the multi-metric evaluation, which include message consistency, transmission reliability, location verification, as well as behavioral patterns. Both direct and indirect trust will be calculated in a hybrid trust model that combines both techniques, and consensus mechanisms will be used to screen transactions and trust updates. The performance in terms of detection of malicious nodes, false positives and whole network stability is compared with the conventional trust models using a simulated VANET environment. A set of comprehensive metrics including latency, throughput, trust convergence, as well as packet delivery ratio are presented, rendering the proposed solution feasible and robust.
Decentralized trust frameworks represent a fundamental transformation in cross-enterprise integration, addressing longstanding challenges in business-to-business interactions. These frameworks leverage Web3 technologies, specifically, Distributed Ledger Technology, Decentralized Identifiers, and Verifiable Credentials to establish inherent trust between organizations without relying on centralized intermediaries. Through cryptographic verification mechanisms, organizations gain enhanced security, verifiable data provenance, reduced reconciliation overhead, and improved operational resilience. The architectural components include a decentralized identity layer providing 99.98% authentication accuracy, credential exchange mechanisms enabling selective disclosure with 99.87% privacy preservation, shared ledger infrastructure ensuring immutable audit trails, and enterprise integration components bridging with existing systems. Implementation patterns such as credential-based API authorization, event-triggered credential issuance, ledger-anchored business processes, and credential-based data exchange deliver substantial improvements in security posture and operational efficiency. Despite significant benefits including 87.3% security enhancement and 73.4% reduction in reconciliation efforts, adoption challenges remain around technical complexity, standards maturity, legacy system integration, and governance frameworks. By addressing these challenges through phased implementation focusing on high-value integration points, organizations can gradually transform their integration landscape toward more secure, transparent, and resilient models that fundamentally change how trust is established in digital business ecosystems.
Yalan Wang, Liqun Chen, Yangguang Tian, Long Meng · 5 authors
Abstract The World Wide Web Consortium (W3C) has established standards for decentralized identities (DIDs) and verifiable credentials (VCs). A DID serves as a unique identifier for an entity, while a VC validates specific attributes associated with the DID holder. To prove ownership of credentials, users generate verifiable presentations (VPs). To enhance privacy, the W3C standards advocate for randomizable signatures in VC creation and zero-knowledge proofs for VP generation. However, these standards face a significant limitation: they cannot effectively verify cross-domain credentials while maintaining anonymity. In this paper, we present Anonymous Verifiable Presentations with Extended Usability (AVPEU), a novel framework that addresses this limitation through the introduction of a notary system. At the technical core of AVPEU lies our proposed randomizable message-hiding signature scheme. We provide both a generic construction of AVPEU and specific implementations based on Boneh–Boyen–Shacham, Camenisch–Lysyanskaya, and Pointcheval–Sanders signature. Our experimental results demonstrate the feasibility of these schemes.
Decentralized Identity (DID) management is pivotal for data security and privacy-preserving in the digital era, yet existing systems face critical challenges, including single point of failure, privacy leakage, and high computational overhead. To address these limitations, this paper proposes a multi-party verifiable trust validation mechanism based on Verifiable Relation Sharing (VRS). The mechanism skillfully integrates Multi-Verifier Zero-Knowledge proofs (MVZK) with Verifiable Secret Sharing (VSS) to securely share secret attribute vectors among multiple verifiers. This enables the generation of Zero-Knowledge Proof (ZKP) of relationships without revealing the actual data. This mechanism ensures that each verifier obtains partial information, thereby effectively defending against single-point attacks, and reducing computational and communication costs through distributed verification. Experimental results confirm the practicality within DID ecosystems, offering a scalable and privacy-preserving solution for multi-party verification and a foundational framework for secure DID verification in blockchain.
Awid Vaziry, Sandro Rodriguez Garzon, Patrick Herbke, Carlo Segat · 5 authors
The intersection of blockchain (distributed ledger) and identity management lacks a comprehensive framework for classifying distributed-ledger-based identity solutions. This paper introduces a methodologically developed taxonomy derived from the analysis of 390 scientific papers and expert discussions. The resulting framework consists of 22 dimensions with 113 characteristics, organized into three groups: trust anchor implementations, identity architectures (identifiers and credentials), and ledger specifications. This taxonomy facilitates the systematic analysis, comparison, and design of distributed-ledger-based identity solutions, as demonstrated through its application to two distinct architectures. As the first methodology-driven taxonomy in this field, this work advances standardization and enhances understanding of distributed-ledger-based identity architectures. It provides researchers and practitioners with a structured framework for evaluating design decisions and implementation approaches.
Tarek Galal, Valeria Tisch, Katja Assaf, Andreas Polze
Railways provide a critical service and operate under strict regulatory frameworks for implementing changes or upgrades. Despite their impact on the public, these frameworks do not define means or mechanisms for transparency towards the public, leading to reduced trust and complex tracking processes. We analyse the German guideline for railway-infrastructural modifications from proposal to approval, using the guideline as a motivating example for modelling decisions in processes using digital signatures and zero-knowledge proofs. Therein, a verifier can verify that a process was executed correctly by the involved parties and according to specification without learning confidential information such as trade secrets or identities of the participants. We validate our system by applying it to the railway process, demonstrating how it realises various rules, and we evaluate its scalability with increased process complexities. Our solution is not railway-specific but also applicable to other contexts, helping leverage zero-knowledge proofs for public transparency and trust.
Federated Learning (FL) has emerged as a transformative paradigm in the field of distributed machine learning, enabling multiple clients such as mobile devices, edge nodes, or organizations to collaboratively train a shared global model without the need to centralize sensitive data. This decentralized approach addresses growing concerns around data privacy, security, and regulatory compliance, making it particularly attractive in domains such as healthcare, finance, and smart IoT systems. This survey provides a concise yet comprehensive overview of Federated Learning, beginning with its core architecture and communication protocol. We discuss the standard FL lifecycle, including local training, model aggregation, and global updates. A particular emphasis is placed on key technical challenges such as handling non-IID (non-independent and identically distributed) data, mitigating system and hardware heterogeneity, reducing communication overhead, and ensuring privacy through mechanisms like differential privacy and secure aggregation. Furthermore, we examine emerging trends in FL research, including personalized FL, cross-device versus cross-silo settings, and integration with other paradigms such as reinforcement learning and quantum computing. We also highlight real-world applications and summarize benchmark datasets and evaluation metrics commonly used in FL research. Finally, we outline open research problems and future directions to guide the development of scalable, efficient, and trustworthy FL systems.