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.
Joy Nnenna Okolo, Adesola Adul-Gafar Arowogbadamu, Samuel Adetayo Adeniji, Rhoda Kalu Tasie
The rapid adoption of mobile AI applications in areas such as healthcare, finance, and personalized services has raised significant concerns about data privacy and security. Traditional centralized machine learning (ML) models require mobile devices to transmit user data to cloud servers, posing risks of data breaches and regulatory non-compliance. Federated learning (FL) addresses these concerns by allowing decentralized AI model training directly on user devices, ensuring that raw data remains private and never leaves the device. However, FL faces security vulnerabilities and performance limitations, including model inversion attacks, data poisoning risks, and high computational overhead. This paper explores key privacy-preserving techniques such as differential privacy, secure aggregation, and homomorphic encryption, which enhance FL security while maintaining model accuracy. Additionally, emerging trends such as blockchain-integrated FL, post-quantum cryptography, and AI-driven optimization are analyzed to highlight the future of privacy-preserving mobile AI ecosystems. By integrating advanced cryptographic techniques and decentralized verification mechanisms, FL can enable scalable, secure, and regulation-compliant AI applications, ensuring a balance between data privacy and AI innovation.
The protection of personal privacy has become a paramount issue in the field of data science, with its significance continuously rising. Differential privacy technology has garnered significant attention for its effectiveness in preserving individual privacy. However, the implementation of differential privacy relies on a degree of trust in the entities or individuals executing the algorithms. This paper proposes an innovative solution: a verifiable differential privacy mechanism based on zero-knowledge proofs. This approach integrates differential privacy with zero-knowledge proof technology to not only verify the correctness of the differential privacy techniques but also enhance the transparency and reliability of the algorithms. Additionally, we have designed a publicly verifiable data release scheme that integrates commitment mechanisms and range proofs, ensuring that the range of published data noise does not exceed predetermined thresholds, thereby ensuring the utility of the data. Compared to other verifiable differential privacy solutions, our approach is unique in that it does not rely on the number of participants but is solely dependent on the precision of the data. This means that our computational cost will not increase with the addition of more participants. Finally, we conducted a performance evaluation of the solution, which only took 700ms to complete a single verification. On average, there was a 6% reduction in expectation and a 40% reduction in variance, demonstrating the enhancement of its data utility and the feasibility and effectiveness in practical applications.
Blockchain technology, a decentralized and immutable ledger, has transformed identity and access management (IAM) by enhancing security, privacy, and trust in digital ecosystems. Ensuring safe authentication and data integrity is made possible by its integration with sophisticated cryptographic techniques like zero-knowledge proofs (ZKPs) and public- key infrastructure (PKI). Other methods include verifiable credentials (VCs) and decentralized identifiers (DIDs). This paper provides a comprehensive analysis of blockchain-based IAM systems, comparing leading blockchain platforms, including Ethereum, Hyperledger Indy, IOTA, and IoTeX, in identity management. The role of blockchain in mitigating identity-related threats, such as identity theft and unauthorized access, is explored through decentralization, immutability, and smart contract automation. Additionally, key security enhancements, including cryptographic mechanisms that strengthen decentralized identity solutions and privacy-preserving authentication, are examined. The potential of blockchain to establish a self-sovereign identity framework that fosters trust, scalability, and security in digital identity ecosystems is highlighted, paving the way for the next generation of identity management solutions.
S Sudhip, Pratyush Sthapit, S. Maheshwari, Samarjit Sahu · 5 authors
Abstract: In today's fast-paced world, tracking personnel has become a necessity for various organizations, especially in industries such as Police Department, and security. The use of Near Field Communication (NFC) devices has emerged as a promising technology for tracking personnel. This paper presents a study on the feasibility and benefits of tracking personnel using NFC devices. Our study is based on NFC (Near Field Communication) which is A wireless communication technology that allows two devices to Exchange data when they are brought into proximity. NFC when used in reader/writer mode NFC device can read From NFC transponders or NFC writer. NFC when used in peerTo-peer mode NFC can be used to exchange information Between two NFC enabled devices and in card emulation mode NFC device can be used with Contactless Card for various Purposes like paying money or exchanging information Security of NFC device can be ensured by various means of Encryption and now we have new web3 technology.
We investigate the impact of anonymity and privacy-preservation on cryptocurrency use. We find that privacy coins, which deploy advanced privacy-preserving technologies to enhance trader anonymity, experience a relative increase in usage compared to non-privacy coins following regulatory interventions aimed at countering illegal activities in cryptocurrency trading and use. However, the adoption of privacy coins decreases relative to non-privacy coins after the introduction of regulations restricting the use of privacy-preserving protocols. These findings underscore the significance of privacy as a driving factor in cryptocurrency adoption.
Property transactions in the UK are increasingly adopting blockchain technology to enhance efficiency, transparency, and security. However, the inherent transparency of blockchain raises significant data privacy risks and regulatory compliance challenges, particularly under the UK General Data Protection Regulation (UK GDPR). This study examines the role of Zero-Knowledge Proofs (ZKPs) in addressing these concerns by enabling transaction validation while preserving confidentiality. Using entropy measures, k-anonymity analysis, and logistic regression, this research quantitatively assesses the privacy risks, effectiveness of ZKPs, and regulatory acceptance in blockchain-based property transactions. The findings reveal that 65.5% of transactions remain highly or moderately identifiable, posing privacy vulnerabilities under UK data protection laws. ZKP-enabled transactions significantly enhance confidentiality, achieving a 92.5% transaction privacy score, compared to 48.3% for non-ZKP transactions. However, these privacy gains come at a 67.8% increase in transaction costs, highlighting a critical trade-off between security and efficiency. Regulatory approval rates for ZKP-based blockchain platforms stand at 72.5%, suggesting a strong potential for compliance advantages. While ZKPs improve privacy and regulatory alignment, challenges remain in terms of computational overhead, transaction costs, and adoption barriers. To facilitate large-scale implementation, this study recommends optimizing zk-Rollups for efficiency, developing clear policy frameworks, and enhancing collaboration between regulators, industry stakeholders, and blockchain developers. These steps are essential to ensuring a balance between privacy, scalability, and compliance, paving the way for secure and legally sound blockchain-based property transactions in the UK.
This study examines the representation and evaluation of Web3 technology in the German music media from 2016 to 2022, focusing on its prevalence, framing, and acceptance in the context of the music sector. Utilizing framing theory and the Technology Acceptance Model, a quantitative content analysis was conducted on articles from various music magazines. The findings indicate a generally positive portrayal of Web3, with significant discussion peaks in 2019, 2021, and 2022. Notably, no coverage was found in music education magazines, suggesting a gap in Web3 engagement in pedagogy. Much of the coverage was in Musikwoche, highlighting Web3’s impact on business aspects like ticketing, copyright, and licensing. The overall positive depiction, juxtaposed with limited critical evaluation, points to the need for a more nuanced discourse. The study underscores implications for balanced media coverage, informed musician engagement with Web3, and the potential for incorporating this technology in music education. It highlights the importance for the music industry of capitalizing on Web3’s positive aspects while practising critical awareness, and it calls for further research to explore the depth of Web3’s influence in music.
The advancement of e-health systems has resulted in substantial enhancements in healthcare delivery via effective data management and accessibility. The use of digital health solutions presents dangers to sensitive health information, including unauthorised access, privacy violations, and security weaknesses. This research presents a blockchain-based paradigm for privacy-preserving authentication and access control specifically designed for e-health systems. The architecture utilises the Ethereum blockchain, smart contracts, blind signatures, Proof of Authority (PoA) consensus, and one-way hash functions to improve data integrity, security, and privacy in a decentralised framework. The proposed methodology addresses computational efficiency and scalability issues via the implementation of lightweight cryptographic techniques, achieving an average authentication delay of 0.059 milliseconds, which represents a 4000-fold improvement compared to current approaches. The model exhibits a significant decrease in memory use, requiring just 0.0198 MB in contrast to the 96.98 MB required by benchmark models, and attains an average signature verification duration of 0.00092 milliseconds. The findings demonstrate the model’s capability for safe, efficient, and scalable applications in e-health, which guarantees privacy and adherence to regulatory norms.
This paper explores the integration of blockchain technology and smart contracts in the development of nextgeneration digital identity solutions. As the demand for secure, privacy-preserving, and user-centric identity management systems increases, blockchain and smart contracts offer a promising framework that enhances transparency, automation, and user control. We outline the methodology employed to assess the effectiveness of blockchain and smart contracts in digital identity management, focusing on aspects such as security, interoperability, and user empowerment. Through comprehensive data analysis, we present the results of our study, demonstrating the potential benefits and challenges associated with implementing blockchain-based identity systems augmented by smart contracts. Our findings contribute to the ongoing discourse on digital identity and provide insights for future research and practical applications.
Personal AI assistants (e.g., Apple Intelligence, Meta AI) offer proactive recommendations that simplify everyday tasks, but their reliance on sensitive user data raises concerns about privacy and trust. To address these challenges, we introduce the Guardian of Data (GOD), a secure, privacy-preserving framework for training and evaluating AI assistants directly on-device. Unlike traditional benchmarks, the GOD model measures how well assistants can anticipate user needs-such as suggesting gifts-while protecting user data and autonomy. Functioning like an AI school, it addresses the cold start problem by simulating user queries and employing a curriculum-based approach to refine the performance of each assistant. Running within a Trusted Execution Environment (TEE), it safeguards user data while applying reinforcement and imitation learning to refine AI recommendations. A token-based incentive system encourages users to share data securely, creating a data flywheel that drives continuous improvement. Specifically, users mine with their data, and the mining rate is determined by GOD's evaluation of how well their AI assistant understands them across categories such as shopping, social interactions, productivity, trading, and Web3. By integrating privacy, personalization, and trust, the GOD model provides a scalable, responsible path for advancing personal AI assistants. For community collaboration, part of the framework is open-sourced at https://github.com/PIN-AI/God-Model.
Introduction The insurance industry has evolved into a global multi-billion-dollar sector, with health insurance gaining prominence due to escalating healthcare costs. This rapid expansion brings heightened risks, including data breaches, fraud, and difficulties in safeguarding sensitive policyholder information. Indonesia’s National Health Insurance (NHI)—one of the largest national insurance programs worldwide—covers over 200 million citizens, aiming to provide universal healthcare. However, this extensive coverage raises substantial concerns about data privacy and traceability, particularly during the claim process, as policyholders currently have limited control over and insight into how their data is accessed and used. Methods To address these challenges, we propose a blockchain-based model designed to enhance policyholders’ private control over data access and improve traceability throughout the NHI claim process. Our approach employs three complementary architectures—functional, logical, and physical—to guide system implementation. The functional architecture is illustrated via a use case diagram that outlines the roles and actions of each participant. The logical architecture employs Business Process Model and Notation (BPMN) diagrams to depict the revised process flow and data movement, while also incorporating a layered design concept. The physical data architecture provides a class diagram detailing data structures and actor relationships. A proof-of-concept prototype was developed to demonstrate the core functionalities of the new system. Results By integrating blockchain technology, our prototype ensures authorized access, bolsters data privacy, and maintains data integrity in the NHI claim workflow. The system’s layered design and use of smart contracts guarantee transparent, tamper-proof record-keeping, while parallelized processes in the logical architecture streamline claims handling. Initial tests of the prototype confirm the feasibility and robustness of the proposed solution, illustrating how blockchain can facilitate traceability and preserve confidentiality. Discussion The blockchain-based design addresses pressing concerns surrounding data security and accountability in large-scale health insurance systems. It allows policyholders to monitor and control their personal information, reducing the likelihood of unauthorized use. Furthermore, the transparent and immutable ledger enables stakeholders to verify data provenance and transactions, enhancing trust. Future work will focus on scalability, regulatory compliance, and integration with existing healthcare IT infrastructures to fully realize the benefits of blockchain in national health insurance programs.
<div> Mixnet protocols are used in electronic voting protocols to mix the ballot box before the tally, to preserve ballots privacy and unlinkabiliy. Whereas proving security properties of the other components of the electronic voting protocols has globally already been done in several logical frameworks and tools, proofs of mixnets remain a real challenge to handle. In this paper we focus on the quite recent CCSA logic, which enables handling of computational security proofs with first-order logics facilities. We enrich the logic to be able to deal with zero-knowledge proofs and rewinding techniques, and provide the first complete proof of Terelius-Wikström mixnet protocol. </div>
Piotr Stolarski, Elżbieta Lewańska, Witold Abramowicz, Erich Schweighofer
This paper examines the alignment of blockchain architecture with the specific requirements of public business registers in Europe. Through a comprehensive literature review, categorization of blockchain models, and analysis of European public business registry cases, this study develops a framework to guide blockchain adoption in public registries. A distributed ledger taxonomy tailored for business registers is presented. This article also introduces a registry mapping procedure and addresses security concerns essential for the transition to blockchain-based architectures. Blockchain technology, popularized in 2009, has evolved into a versatile tool with applications across various domains, including the public sector, where its potential for decentralized solutions is especially promising. In particular, strategic systems that constitute the backbone of the legal and economic order require special considerations providing superior quality and the appropriate level of security. This study proposes a categorization method, outlines a registry mapping procedure, and discusses security concerns integral to blockchain implementation in public registries. The potential of blockchain technology to change the architecture of public business registries is also discussed.
Federated Learning (FL) has emerged as a transformative approach to distributed machine learning, enabling the collaborative training of models across decentralized and private datasets. Unlike traditional centralized learning paradigms, FL ensures data privacy by keeping raw data localized on client devices while leveraging aggregated updates to build global models. This survey explores the critical aspects of efficient federated learning, including communication reduction, robustness to system and data heterogeneity, and scalability in real-world applications. We discuss key techniques such as model compression, asynchronous updates, personalized learning, and robust aggregation to address challenges posed by resource-constrained devices, non-IID data distributions, and adversarial environments. Applications of FL across diverse domains, including healthcare, finance, smart cities, and autonomous systems, highlight its potential to transform industries while preserving privacy and compliance with regulatory frameworks. The survey also identifies open challenges in scalability, privacy guarantees, fairness, and ethical considerations, providing future research directions to address these gaps. As FL continues to evolve, it holds the promise of enabling privacy-preserving, collaborative intelligence on a global scale, fostering innovation while addressing critical societal and technical challenges.
Olanrewaju Oluwaseun Ajayi, Chisom Elizabeth Alozie, Olumese Anthony Abieba, Joshua Idowu Akerele · 5 authors
Blockchain technology has emerged as a transformative force within the financial technology (Fintech) sector, offering unprecedented opportunities for efficiency, transparency, and security. However, its adoption also brings forth new challenges and vulnerabilities, particularly in the realm of cybersecurity. This review explores the dynamic landscape of Blockchain Technology and Cybersecurity in Fintech, highlighting both the opportunities it presents and the vulnerabilities it introduces. Blockchain technology, most notably recognized as the underlying framework for cryptocurrencies like Bitcoin and Ethereum, operates on a decentralized ledger system, enabling secure and immutable transactions. In Fintech, this technology promises enhanced transactional speed, reduced costs, and increased transparency, revolutionizing traditional banking and payment systems. Nevertheless, the decentralized nature of blockchain networks, while offering resilience against single points of failure, also poses unique cybersecurity risks. Smart contracts, self-executing contracts with the terms of the agreement directly written into code, introduce vulnerabilities such as code bugs and exploits. Moreover, the anonymity associated with blockchain transactions has raised concerns regarding illicit activities, money laundering, and terrorist financing. In response to these challenges, the intersection of Blockchain Technology and Cybersecurity in Fintech offers opportunities for innovation. Advanced cryptographic techniques, such as multi-signature authentication and zero-knowledge proofs, are being leveraged to enhance security and privacy in blockchain-based systems. Additionally, regulatory frameworks are evolving to address the emerging risks associated with Fintech innovations, ensuring compliance and consumer protection. While Blockchain Technology presents promising opportunities for revolutionizing Fintech, its integration must be accompanied by robust cybersecurity measures to mitigate vulnerabilities and safeguard against potential threats. Collaborative efforts between industry stakeholders, regulators, and cybersecurity experts are imperative to foster a secure and resilient ecosystem for blockchain-based financial services.
Abstract Most algorithms deployed in healthcare do not consider gender and sex despite the effect they have on individuals’ health differences. Missing these dimensions in healthcare information systems is a point of concern, as neglecting these aspects will inevitably perpetuate existing biases, produce far from optimal results, and may generate diagnosis errors. An often-overlooked community with distinct care values and needs is LGBTQ+ older adults, who have traditionally been under-surveyed in healthcare and technology design. This article investigates the implications of missing gender and sex considerations in distributed ledger technologies for LGBTQ+ older adults. By using the value sensitive design (VSD) methodology, our contribution shows that many value meanings dear to marginalized communities are not considered in the design of the blockchain, such as LGBTQ+ older adults’ interpretations of trust, privacy, and security. By highlighting the LGBTQ+ older population values, our contribution alerts us to the potential discriminatory implications of these technologies, which do not consider the gender and sex differences of marginalized, silent populations. Focusing on one community throughout, we emphasize the need for a holistic, VSD approach for the development of ledger technologies for healthcare, including the values of everyone within the healthcare ecosystem.
Abstract This paper evaluates Dynamic-IoTrust access control that integrated blockchain and trust value to meet the requirements of dynamic, secure, and distributed access control in the IoT environment. Dynamic-IoTrust intended to overcome the issues related to dynamic access control in IoT by limit authorized users’ access based on the trust value and user misbehavior. In particular, the system contains three kinds of smart contracts, multiple Main Smart Contract (MSC), one Register Contract (RC), and one Judging Contract (JC). Dynamic-IoTrust provides predefined static policy and dynamic trust value. The performance of Dynamic-IoTrust is analyzed by calculating the cost consumption rate of smart contracts and their function. A comparison is made between the existing systems and Dynamic-IoTrust. The results illustrate the transaction and execution costs of smart contracts.
This systematic literature review compares two leading selective-disclosure primitives for decentralised identity-BBS+ signatures and Selective-Disclosure JSON Web Tokens (SD-JWT)-to clarify their suitability for privacypreserving credentials. Following Kitchenham's protocol, 226 records from 2017-2025 were screened across IEEE, ACM, SpringerLink, ScienceDirect, IETF and W3C repositories, yielding 31 primary studies with empirical data. Quantitative synthesis shows that BBS+ derived proofs remain constant-size at roughly 140 bytes and verify in about 12 ms on consumer hardware, whereas SD-JWT presentations grow with the number of revealed claims but still verify in under 10 ms for typical twoclaim use cases. Qualitative analysis confirms BBS+ provides strong unlinkability, predicate proofs and zero-knowledge disclosure, while SD-JWT offers seamless integration with existing JOSE/OAuth infrastructures yet carries correlation risk due to stable salted digests. Standardisation progress is comparable: the BBS+ cryptosuite reached W3C Candidate Recommendation in April 2025, and SD-JWT is in late-stage IETF review. The review concludes that privacy-critical scenarios such as age-gated services favour BBS+, whereas high-throughput web applications benefit from SD-JWT; consequently, hybrid wallet support for both formats is recommended. Future research should tackle scalable revocation, post-quantum migration and multi-credential aggregation to sustain long-term trust and interoperability.
Digital identity has become one of the most pressing governance challenges of the 21st century. This paper argues that digital identity is not optional but inevitable, driven by four converging forces: privacy leakage, AI synthesis, corporate capture, and geopolitical vulnerability. Drawing on political philosophy (Rousseau, Rawls, Foucault, Habermas), comparative case analysis (Estonia, India, China), and emerging technical frameworks (zero-knowledge proofs, decentralized identity), the paper analyzes the opportunities and perils of digital ID systems. It proposes a Digital Social Contract as the normative and institutional framework for governing them. The paper concludes that the decisive question is not whether digital IDs will exist, but how they will be governed — and that only a robust Digital Social Contract, grounded in democratic legitimacy, institutional accountability, and adaptive governance, can ensure that digital identity serves citizens rather than controls them.
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.