Shoubai Nie, Jingjing Ren, Rui Wu, Pengchong Han · 6 authors
Within the framework of 6G networks, the rapid proliferation of Internet of Things (IoT) devices, coupled with their decentralized and heterogeneous characteristics, presents substantial security challenges. Conventional centralized systems face significant challenges in effectively managing the diverse range of IoT devices, and they are inadequate in addressing the requirements for reduced latency and the efficient processing and analysis of large-scale data. To tackle these challenges, this paper introduces a zero-trust access control framework that integrates blockchain technology with inner-product encryption. By using smart contracts for automated access control, a reputation-based trust model for decentralized identity management, and inner-product encryption for fine-grained access control, the framework ensures data security and efficiency. Firstly, smart contracts are employed to automate access control, and software-defined boundaries are defined for different application domains. Secondly, through a trust model based on a consensus algorithm of node reputation values and a registration-based inner-product encryption algorithm supporting fine-grained access control, zero-trust self-sovereign enhanced identity management in the 6G environment of the Internet of Things is achieved. Furthermore, the use of multiple auxiliary chains for storing data across different application domains not only mitigates the risks associated with data expansion but also achieves micro-segmentation, thereby enhancing the efficiency of access control. Finally, empirical evidence demonstrates that, compared with the traditional methods, this paper's scheme improves the encryption efficiency by 14%, reduces the data access latency by 18%, and significantly improves the throughput. This mechanism ensures data security while maintaining system efficiency in environments with large-scale data interactions.
Yang Liu, Ru Huo, Ningjie Gao, Cheng Chi · 5 authors
In order to address the challenges encountered in the current Industrial Internet of Things scenarios, such as single points of failure, difficulties in ensuring data privacy and integrity, and a lack of access control, a blockchain-based data security exchange architecture was proposed. To ensure the privacy of industrial data, a data exchange process based on public key encryption and keyword search was introduced. Industrial data is encrypted multiple times and uploaded to the blockchain network. Users retrieve ciphertext from the cloud server after obtaining the key through the blockchain and then decrypt it. To achieve flexible access control, a zero-knowledge proof-based access control mechanism was proposed, utilizing Pedersen commitments and zero-knowledge proofs for access permission issuance, validation, and revocation. Additionally, various forms of smart contracts were proposed for secure data exchange, user authentication, access authorization, and data integrity verification. Finally, a system prototype was built and experimental results confirmed the superiority of the proposed approach.
Ilia Shumailov, Daniel Ramage, Sarah Meiklejohn, Peter Kairouz · 7 authors
We often interact with untrusted parties. Prioritization of privacy can limit the effectiveness of these interactions, as achieving certain goals necessitates sharing private data. Traditionally, addressing this challenge has involved either seeking trusted intermediaries or constructing cryptographic protocols that restrict how much data is revealed, such as multi-party computations or zero-knowledge proofs. While significant advances have been made in scaling cryptographic approaches, they remain limited in terms of the size and complexity of applications they can be used for. In this paper, we argue that capable machine learning models can fulfill the role of a trusted third party, thus enabling secure computations for applications that were previously infeasible. In particular, we describe Trusted Capable Model Environments (TCMEs) as an alternative approach for scaling secure computation, where capable machine learning model(s) interact under input/output constraints, with explicit information flow control and explicit statelessness. This approach aims to achieve a balance between privacy and computational efficiency, enabling private inference where classical cryptographic solutions are currently infeasible. We describe a number of use cases that are enabled by TCME, and show that even some simple classic cryptographic problems can already be solved with TCME. Finally, we outline current limitations and discuss the path forward in implementing them.
Federated Learning (FL) is a distributed machine learning paradigm that enables collaborative model training across decentralized devices while preserving data privacy. It addresses critical challenges in privacy, scalability, and data ownership, making it a promising approach for applications in healthcare, IoT, and finance. However, practical implementation of FL faces several efficiency bottlenecks, including communication overhead, system and data heterogeneity, and security vulnerabilities. This paper provides a comprehensive survey of state-of-the-art techniques aimed at enhancing the efficiency of FL. Key methods such as model compression, including pruning, quantization, and tensor decomposition, are explored to address communication constraints. Strategies to mitigate data and system heterogeneity, including personalized FL and resource-aware training, are discussed alongside advancements in privacy-preserving mechanisms like differential privacy and secure aggregation. We also examine scalability solutions, including hierarchical and decentralized FL, to enable large-scale deployment. The survey highlights open challenges and emerging opportunities in FL, offering insights into future research directions for building efficient and robust federated systems.
The integration of Social Media (SM) and the Internet of Things (IoT) is gradually transforming the activities of SM users into valuable data streams that can be analyzed using Machine Learning (ML) algorithms. Federated Learning (FL) has been widely employed to predict user and anomaly behaviors from distributed systems. However, FL encounters substantial security challenges, particularly within the context of SM-integrated IoT systems, known as SM 3.0 systems. These challenges encompass issues of accountability and vulnerabilities that render them susceptible to various cyberattacks, including single-point-of-failure, free-riding, model inversion, and poisoning attacks. We propose a Blockchain-enabled FL with Smart Contracts (SC) (BFL-SC) framework. To coordinate the learning process, track participants’ contributions and reward the participants transparently, an SC-based FL is constructed as an incentive mechanism that combats free-riding attacks and enables automated and auditable rewarding of the participants. Also, to conceal the original data points and mitigate the impact of model inversion attacks, a Differentially Privacy-based Perturbation (DPP) mechanism is proposed. To address potential poisoning attacks, a thorough verification protocol is suggested. The experimental results obtained from two datasets, namely SM 3.0 and Human Activity Recognition (HAR), show that the BFL-SC framework can achieve high utility with a precision of 96.95% over the SM 3.0 dataset and 90.14% over the HAR dataset while adhering to privacy and efficiency standards, compared with compelling techniques.
The proliferation of Internet of Things (IoT) devices across multiple domains has heralded an era of unprecedented connectivity and data exchange. Fog computing enhances edge-network processing, enabling real-time data analysis and prompt responses. However, ensuring secure and trustworthy communication among these devices remains a paramount concern. In fog-enabled IoT environments, securing communication among users, IoT devices, gateways, and fog nodes is of paramount importance to prevent unauthorized access and ensure data integrity and confidentiality. Additionally, users can control and deliver instructions to IoT devices remotely. Hence, we propose a blockchain-assisted authentication protocol tailored specifically for fog-enabled IoT environments to verify the user’s identity prior to accessing the IoT devices. The proposed protocol leverages cutting-edge crypto primitives like elliptic curve cryptography, hash functions, and blockchain to establish secure communication between users and IoT devices. Furthermore, we evaluate the proposed scheme through formal (Scyther) and informal analysis, demonstrating its efficacy in mitigating well-known attacks. On the other hand, the proposed protocol exhibits robustness against relevant protocols in terms of communication and computational aspects, as well as reliability for real-world fog-enabled IoT applications.
Consumer electronics devices (CEDs) are becoming increasingly interconnected and integrated into our daily lives. Thus, the demand for seamless communication, enhanced security, and reliable performance has increased. However, the widespread adoption of these devices raises significant concerns regarding security and privacy in computing, especially when collecting and processing sensitive consumer data. To address these challenges, robust security mechanisms are necessary to protect this sensitive data. In response to these challenges, Blockchain-Enabled Federated Learning for Consumer Electronics Devices (BFLCED) is proposed to make CEDs more secure and privacy-preserving. The combination of blockchain and federated learning (FL) provides a robust solution for real-world CEDs where data privacy and security are most important. The proposed BFLCED ensures devices are authenticated and communicated securely to maintain data integrity and confidentiality during model training. It generates unique identities using the Lightweight Elliptic Curve Digital Signature Algorithm (LECDSA) and digital signatures for data integrity. Parallelly, smart contracts are employed to verify device identities & data integrity automatically and enable secure communication among devices. Data privacy is maintained during model aggregation by securely aggregating updates using encryption and multi-party computation (MPC). In the end, a security analysis is conducted to evaluate the effectiveness of the proposed mechanisms in safeguarding CEDs against potential threats and vulnerabilities. Furthermore, the proposed BFLCED transforms automation and personalization by securely connecting CEDs to our daily lives.
The convergence of artificial intelligence and blockchain technology offers a compelling paradigm for deploying secure, auditable, and decentralisedintelligent systems in distributed environments where trust between participants cannot be assumed. Existing AI deployment frameworks lackimmutable audit trails, transparent model governance, and cryptographic integrity guarantees — requirements increasingly mandated by regulatoryframeworks including the EU AI Act and GDPR. This study presents ChainMind, a blockchain-enabled secure intelligent system frameworkintegrating smart contract-governed model lifecycle management, federated learning with on-chain gradient verification, and zero-knowledge proof(ZKP)-based inference auditing for privacy-preserving accountability. ChainMind was deployed and evaluated across three distributed intelligentsystem applications: a decentralised medical AI consortium (6 European hospitals, 284,000 patient records), a cross-border financial fraud detectionnetwork (4 banks, Germany and Italy), and a smart city data marketplace (Stuttgart urban IoT network, 12,400 sensors). ChainMind achieved modeltampering detection accuracy of 99.97%, federated learning convergence within 18.3% fewer rounds than standard FedAvg under adversarialgradient poisoning, and ZKP inference verification latency of 47.3 ms — compatible with real-time deployment. These results establish ChainMind asa technically viable and regulatory-compliant framework for blockchain-enabled secure AI in distributed environments.
Folding schemes are an exciting new primitive, transforming the task of performing multiple zero-knowledge proofs of knowledge for a relation into performing just one zero-knowledge proof, for the same relation, and a number of cheap inclusion-proofs. Recently, folding schemes have been used to amortize the cost associated with proving different statements to multiple distinct verifiers, which has various applications. We observe that for these uses, leaking information about the statements folded together can be problematic, yet this happens with previous constructions. Towards resolving this issue, we give a natural definition of privacy preserving folding schemes, and what security they should offer. To construct privacy preserving folding schemes, we first define statement hiders, a primitive which might be of independent interest. In a nutshell, a statement hider hides an instance of a relation as a new instance in the same relation. The new instance is in the relation if and only if the initial instance is. With this building block, we can utilize existing folding schemes to construct a privacy preserving folding scheme, by first hiding each of the statements. Folding schemes allow verifying that a statement was folded into another statement, while statement hiders allow verifying that a statement was hidden as another statement.
ABSTRACT Healthcare systems are highly sensitive to cyberattacks as these systems possess most of the sensitive information compared to other systems relying on internet facilities. Due to the stronger security merits and efficiency of blockchain, it is integrated with the healthcare sector to ensure reliable data transfer. However, to improve the reliability and efficiency of the integrated system, a permissioned blockchain‐based security framework combining several techniques is proposed. To enable storing and validating blocks containing medical data on the blockchain, the miner is administered using the delegated proof of stake (DPoS) consensus protocol. This protocol is efficient in choosing the miner from the list of participants. Then, the blocks are created using recursive indexing with an Even–Rodeh (RI‐ER) coding hashing scheme. This algorithm is an indexing scheme that is much more efficient than the normal hashing algorithms. The validation process is carried out by the miner using the hash values provided to the users. By using the kidney disease dataset from Kaggle, the performance of the proposed method is evaluated. The performance analysis proved the effectiveness of the proposed approach compared to other schemes.
This paper presents a comprehensive review of the technological advancements, practical applications, inherent challenges, and emerging trends shaping the field of mobile biometrics. Over the past decade, the domain has evolved from basic fingerprint sensors to sophisticated multimodal systems leveraging AI-driven physiological and behavioral biometrics. The analysis examines the vast opportunities in finance, health-care, and digital identity management, while emphasizing the critical need to address privacy, security, regulatory, and ethical concerns. Furthermore, the study underscores the importance of collaborative efforts, highlighting promising future directions such as decentralized biometric storage and blockchain integration to enable secure and user-centric mobile experiences.
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.
Ahmed Ayoub Bellachia, Mouhamed Amine Bouchiha, Yacine Ghamri-Doudane, Mourad Rabah
Blockchain-based Federated Learning (BFL) is an emerging decentralized machine learning paradigm that enables model training without relying on a central server. Although some BFL frameworks are considered privacy-preserving, they are still vulnerable to various attacks, including inference and model poisoning. Additionally, most of these solutions employ strong trust assumptions among all participating entities or introduce incentive mechanisms to encourage collaboration, making them susceptible to multiple security flaws. This work presents VerifBFL, a trustless, privacy-preserving, and verifiable federated learning framework that integrates blockchain technology and cryptographic protocols. By employing zero-knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) and in-crementally verifiable computation (IVC), VerifBFL ensures the verifiability of both local training and aggregation processes. The proofs of training accuracy and aggregation are verified on-chain, guaranteeing the integrity and auditability of each participant's contributions. To protect training data from inference attacks, VerifBFL leverages differential privacy. Finally, to demonstrate the efficiency of the proposed protocols, we built a proof of concept using emerging tools. The results show that generating proofs for local training and aggregation in VerifBFL takes less than 81s and 2s, respectively, while verifying them on-chain takes less than 0.6s.
Yushintia Pramitarini, Ridho Hendra Yoga Perdana, Kyusung Shim, Beongku An
In this paper, we propose a novel federated blockchain (FedChain)-based clustering protocol to enhance network security and connectivity in flying ad hoc networks (FANETs) with cell-free massive MIMO (CF-mMIMO). By leveraging blockchain technology and federated learning (FL), the cluster can be protected against Sybil attacks, enabling secure cluster formation without increasing the number of control packets. We formulate the cost function maximization problem based on cross-layer design, which integrates physical layer information (mobility, position, channel capacity, and remaining energy) and network layer parameters (connectivity) to optimize the formation of stable clusters with minimal control overhead. Furthermore, we select the optimal cluster heads (CHs) based on the highest remaining energy and velocity-constrained criteria, ensuring long-term stability. To solve the security issue, blockchain technology is adopted to validate transactions among nodes and ensure secure formation by distinguishing legitimate users and Sybil attack nodes. Additionally, we develop a novel FL framework to predict and distinguish node status in real time without additional control packets, improving security and control overhead performance during cluster formation. Simulation results demonstrate that the proposed FedChain-based clustering protocol outperforms the lowest ID (LI), high connectivity degree (HCD), and conventional blockchain-based clustering (CBC) protocols in terms of connectivity, control overhead, and security performance. The results highlight that the FedChain-based clustering protocol provides robust security and connectivity, making it well-suited for dynamic FANET environments.
Advanced connection and autonomous features are being made possible by the Internet of Vehicles (IoV), which is causing a revolution in transportation. Strong security measures are required, however, because the prevalence of connected devices also increases the likelihood of cyberattacks and data breaches. This study introduces a new method for protecting IoV networks, which combines Blockchain technology with Quantum Key Distribution (QKD), creating a security architecture with two layers. Internet of Vehicles (IoV) technologies enable autonomous driving and real-time data exchange by connecting vehicles to infrastructure and one another. These advancements make things safer and more efficient, but they also put sensitive information at risk of cyberattacks. Modern security measures are essential since traditional encryption methods are becoming more and more insecure. To provide encryption that is theoretically unbreakable, the suggested system uses QKD to create and distribute cryptographic keys based on principles of quantum mechanics. To improve trust and transparency, blockchain technology is used to record these keys and any subsequent transactions in an immutable, distributed ledger. A hybrid architecture, with QKD securing the key exchange and Blockchain ensuring the integrity and authenticity of the communication, is designed as part of the integration process. Improved security and speed have been shown in simulations and prototype implementations of the QKD-Blockchain architecture in IoV networks. By preventing eavesdropping and key interception, the QKD technique kept the communication channel secure. With an average delay of only about 2 milliseconds, QKD performed admirably and was well below the permitted range for real-time vehicular communications. On average, validation durations for transactions were 5 milliseconds, which was a little overhead due to blockchain integration. The system efficiently handled up to 10,000 transactions per second without affecting security or performance, proving that it can serve massive IoV networks, according to scalability testing. Under high-load scenarios, the framework maintained consistent performance and security, proving its robustness in stress tests. Together, QKD and Blockchain provide a scalable and trustworthy option for future vehicular communication networks, and these results show how feasible and robust it is to use them to protect IoV systems. An intriguing approach to the security issues plaguing IoV systems is the integration of QKD with Blockchain technology. An unparalleled level of protection against cyber threats is provided by the dual-layered system, which guarantees strong encryption and data integrity. This fresh method may lead to improved and more trustworthy IoV networks by establishing new benchmarks for secure vehicular communication. In order to optimize the implementation and tackle any new issues that may arise, more research and development should be conducted.
The rapid development of Internet of Things technology has promoted the popularization of Internet of Vehicles, and its safety and reliability have become the focus of intelligent transportation system research. Vehicle-road collaboration relies on the collaborative computing and storage resources of the vehicle on-board unit (OBU), which are usually limited. When the vehicle in the edge area needs to do computing tasks such as intelligent driving, but its own computing resources are insufficient. Therefore, it needs other computing resources from idle vehicles and road side unit (RSU). This resource sharing can get additional computing resources to complete the task, and can be more convenient to complete the computing task quickly. Most current studies consider this type of resource sharing as a vehicle-to-vehicle (V2V) network transaction, aiming to stimulate the enthusiasm of vehicle sharing and optimize the utilization of computing resources in edge areas. However, the traditional blockchain transaction mode exposes serious privacy disclosure risks in vehicle networking resource transactions, including the openness and transparency of user identity, transaction details, and transaction addresses, which poses great challenges to data security. Therefore, this study innovatively proposed a blockchain-based privacy protection scheme for vehicle networking resource transaction details. By introducing committed value protection, zero-knowledge proof technology and constructing temporary transaction addressed mechanism. The scheme realized the comprehensive privacy protection of transaction funds, transaction details and transaction addresses, which could effectively avoid the disclosure of users' sensitive information. Compared with the existing methods, the proposed scheme not only greatly enhanced the privacy protection capability, but also ensured the efficiency and security of transaction verification through zero-knowledge proof, avoiding the direct exposure of private keys. Meanwhile, the experimental verification demonstrates that the scheme not only enhances the level of privacy protection but also does not augment the supplementary processing burden. Furthermore, it is evident that the scheme meets the rigorous requirements for real-time resource transactions in the Internet of Vehicles.
Shaista Ashraf Farooqi, Aedah Abd Rahman, Amna Saad
The advancement of decentralized, real-time data collection through the Internet of Medical Things is transforming the healthcare industry. However, this innovation brings forth significant privacy, security, and scalability challenges. Federated Learning offers a reliable solution by enabling distributed machine learning while preserving data localization. This paper introduces two frameworks-Federated Learning combined with Differential Privacy and Blockchain-enhanced Federated Learning-to enhance robustness in IoMT systems. We compare these frameworks theoretically, evaluating their effectiveness in mitigating risks related to data confidentiality, adversarial resilience, scalability, and computational efficiency. FL-DP provides formal privacy guarantees through differential privacy techniques but is limited by the need to manage the privacy budget (E), especially in large-scale deployments. Alternatively, Blockchain-based FL maintains data integrity and decentralized trust using consensus mechanisms such as Proof of Work and Proof of Stake, but it encounters challenges related to scalability and computational efficiency. Our findings suggest that the choice between FL-DP and Blockchain-based FL depends on the specific security and privacy requirements of the IoMT application. FL-DP is better suited for privacy-critical applications where strict data confidentiality is paramount, while Blockchain-based FL is more appropriate when data integrity and trust are the primary concerns.
Federated Learning (FL) has emerged as a promising paradigm for secure data sharing in Industrial Internet of Things (IIoT), enabling collaborative model training without direct exchange of raw data. However, recent studies have shown that FL still suffers from privacy vulnerabilities, where adversaries can reconstruct sensitive information by analyzing shared model parameters. Although several privacy-preserving FL (PPFL) schemes have been proposed to address these challenges, they primarily focus on protecting local model privacy, with limited attention to protecting global model confidentiality during aggregation. Additionally, their reliance on centralized aggregation servers introduces risks of single points of failure. To address these challenges, we propose a novel privacy-preserving blockchain-based FL framework (PBFL) that integrates blockchain, homomorphic encryption (HE), and a single masking. Specifically, PBFL employs HE to enable secure model training within the ciphertext domain, ensuring global model confidentiality. The single masking technique allows clients to apply unique random masks to their encrypted local model updates, enabling secure aggregation while preserving local privacy. Additionally, PBFL leverages blockchain for decentralized aggregation and encrypted model storage, effectively mitigating the risks associated with centralized servers. Experimental results demonstrate that PBFL achieves comparable model accuracy to state-of-the-art solutions while providing enhanced privacy protection. Furthermore, even with a client dropout rate of up to 30%, PBFL outperforms other blockchain-based PPFL methods in terms of computational and communication efficiency.
Machine learning has achieved remarkable results in numerous fields, demonstrating strong momentum and promising prospects for future development. However, machine learning is facing issues related to data security. User data contains a vast amount of sensitive personal information, and once privacy is breached, users may not only suffer from harassment but also face threats to their lives and property security. As a result, users’ willingness and trust in sharing local raw data are gradually decreasing. In response to this situation, federated learning technology has emerged, which enables efficient training of decentralized data through distributed machine learning methods while protecting users’ data privacy. Traditional federated learning systems suffer from issues such as single points of failure and lack of trust. Blockchain, as a decentralized, traceable, and tamper-resistant distributed ledger technology, provides a new solution for federated learning. It records every update of the global model, verifies and tracks local updates, and is equipped with a fair incentive mechanism. Based on these ideas, this paper proposes a federated learning framework combined with blockchain, aiming to address data security issues in federated learning.
This study explores how blockchain technology can be strategically applied to improve data security and privacy across key sectors, including healthcare, finance, and supply chain management. Through a modular, Python-based implementation using permissioned blockchains, the research evaluates performance metrics such as transaction speed, data integrity, encryption, and anomaly detection. Results indicate that blockchain systems significantly enhance data protection by ensuring immutability, decentralization, and real-time validation. In healthcare, the system effectively flagged anomalous records; in finance, it supported secure, high-volume transactions; and in supply chains, it enabled transparent, tamper-proof tracking. Despite promising outcomes, challenges such as interoperability, scalability, and regulatory compliance remain. The study also highlights ethical considerations, particularly in balancing transparency with privacy. Overall, blockchain emerges as a robust and adaptable solution for modern data security needs. The findings provide a foundation for further research and practical guidance for organizations seeking to adopt blockchain in sensitive and high-risk data environments.
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.