The data economy has arisen in most developed countries. Instruments and tools to extract knowledge and value from large collections of data are now available and enable new industries, business models, and jobs. However, the current data market is asymmetric and prevents companies from competing fairly. On the one hand, only very specialized digital organizations can manage complex data technologies such as Artificial Intelligence and obtain great benefits from third-party data at a very reduced cost. On the other hand, datasets are produced by regular companies as valueless sub-products that assume great costs. These companies have no mechanisms to negotiate a fair distribution of the benefits derived from their industrial data, which are often transferred for free. Therefore, new digital data-driven marketplaces must be enabled to facilitate fair data trading among all industrial agents. In this paper, we propose a blockchain-enabled solution to monetize industrial data. Industries can upload their data to an Inter-Planetary File System (IPFS) using a web interface, where the data are randomized through a privacy-preserving algorithm. In parallel, a blockchain network creates a Non-Fungible Token (NFT) to represent the dataset. So, only the NFT owner can obtain the required seed to derandomize and extract all data from the IPFS. Data trading is then represented by NFT trading and is based on fungible tokens, so it is easier to adapt prices to the real economy. Auctions and purchases are also managed through a common web interface. Experimental validation based on a pilot deployment is conducted. The results show a significant improvement in the data transactions and quality of experience of industrial agents.
The rapid development of large machine learning (ML) models requires a massive amount of training data, resulting in booming demands of data sharing and trading through data markets. Traditional centralized data markets suffer from low level of security, and emerging decentralized platforms are faced with efficiency and privacy challenges. In this paper, we propose OmniLytics+, the first decentralized data market, built upon blockchain and smart contract technologies, to simultaneously achieve 1) data (resp., model) privacy for the data (resp. model) owner; 2) robustness against malicious data owners; 3) efficient data validation and aggregation. Specifically, adopting the zero-knowledge (ZK) rollup paradigm, OmniLytics+ proposes to secret share encrypted local gradients, computed from the encrypted global model, with a set of untrusted off-chain servers, who collaboratively generate a ZK proof on the validity of the gradient. In this way, the storage and processing overheads are securely offloaded from blockchain verifiers, significantly improving the privacy, efficiency, and affordability over existing rollup solutions. We implement the proposed OmniLytics+ data market as an Ethereum smart contract [41]. Extensive experiments demonstrate the effectiveness of OmniLytics+ in training large ML models in presence of malicious data owner, and the substantial advantages of OmniLytics+ in gas cost and execution time over baselines.
In the ever-evolving realm of cybersecurity, the increasing integration of Metaverse systems with cutting-edge technologies such as Artificial Intelligence (AI), Blockchain, and Cloud Computing presents a host of new opportunities alongside significant challenges. This article employs a methodological approach that combines an extensive literature review with focused case study analyses to examine the changing cybersecurity landscape within these intersecting domains. The emphasis is particularly on the Metaverse, exploring its current state of cybersecurity, potential future developments, and the influential roles of AI, blockchain, and cloud technologies. Our thorough investigation assesses a range of cybersecurity standards and frameworks to determine their effectiveness in managing the risks associated with these emerging technologies. Special focus is directed towards the rapidly evolving digital economy of the Metaverse, investigating how AI and blockchain can enhance its cybersecurity infrastructure whilst acknowledging the complexities introduced by cloud computing. The results highlight significant gaps in existing standards and a clear necessity for regulatory advancements, particularly concerning blockchain’s capability for self-governance and the early-stage development of the Metaverse. The article underscores the need for proactive regulatory involvement, stressing the importance of cybersecurity experts and policymakers adapting and preparing for the swift advancement of these technologies. Ultimately, this study offers a comprehensive overview of the current scenario, foresees future challenges, and suggests strategic directions for integrated cybersecurity within Metaverse systems utilising AI, blockchain, and cloud computing.
Unlabelled: The convergence of artificial intelligence (AI), blockchain technology, and health care represents one of the most transformative yet technically challenging frontiers in computational medicine. As health care systems adopt data-driven paradigms for precision medicine and clinical decision support, the need for secure, privacy-preserving, and collaborative learning frameworks has become critical. This tutorial introduces a comprehensive, clinically oriented, and compliance-aware framework integrating federated learning (FL) and blockchain for secure and privacy-preserving health care analytics. FL enables collaborative training across distributed institutions without raw data sharing, in alignment with privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). However, FL remains vulnerable to model poisoning and gradient leakage. To address these risks, we introduce blockchain-based FL (BCFL), which leverages blockchain's immutable ledger and decentralized consensus to enhance trust, verifiability, and auditability. The tutorial's main contributions include (1) a taxonomy of diverse medical data types and their FL requirements; (2) three integration architectures (fully coupled, semicoupled, and loosely coupled) analyzed for security, scalability, and regulatory compliance; (3) a security analysis of health care-specific vulnerabilities and mitigation strategies using advanced cryptography, such as zero-knowledge proofs, homomorphic encryption, and differential privacy; and (4) a regulatory compliance framework addressing HIPAA, GDPR, and United States Food and Drug Administration guidelines for AI-enabled medical devices. We demonstrate BCFL's relevance across major health care applications, including disease prediction, medical imaging, patient monitoring, and drug discovery, and highlight emerging research directions such as quantum-resilient cryptography, scalable interoperability, and automated compliance. This tutorial serves as a foundational resource for advancing secure, compliant, and collaborative AI in health care; fostering privacy-preserving analytics; and improving patient outcomes.
SAQIB AHAD KHAN -, Sona Mohammad Idrees Shamshuddin -, N. Srinivasan, G Kalaiarasi - · 5 authors
The demands of a hyperconnected society that demand increased security, transparency, and user autonomy cause traditional access control to crumble. This abstract investigates how combining blockchain technology with artificial intelligence could revolutionize access control systems. By removing single points of failure and increasing accountability, blockchain's distributed ledger technology (DLT) creates irreversible trust via a shared, tamper-proof database of rights and transactions. By automating policies, smart contracts give people command over their digital assets. AI adds intelligence and flexibility. Real-time machine learning systems detect anomalies, dynamically assess behavior, and modify policy. Access requests are filtered by AI-driven risk assessment, and sensitive resources are protected by improved identity verification. The combination of AI's dynamic powers and blockchain's unchangeable base opens the door to a future where safe, user-focused access is commonplace.
Yinqiu Liu, Hongyang Du, Dusit Niyato, Jiawen Kang · 7 authors
Mobile AI-Generated Content (AIGC) has achieved great attention in unleashing the power of generative AI and scaling the AIGC services. By employing numerous Mobile AIGC Service Providers (MASPs), ubiquitous and low-latency AIGC services for clients can be realized. Nonetheless, the interactions between clients and MASPs in public mobile networks, pertaining to three key mechanisms, namely MASP selection, payment scheme, and fee-ownership transfer, are unprotected. In this paper, we design the above mechanisms using a systematic approach and present the first blockchain to protect mobile AIGC, called ProSecutor. Specifically, by roll-up and layer-2 channels, ProSecutor forms a two-layer architecture, realizing tamper-proof data recording and atomic fee-ownership transfer with high resource efficiency. Then, we present the Objective-Subjective Service Assessment (OS^{2}A) framework, which effectively evaluates the AIGC services by fusing the objective service quality with the reputation-based subjective experience of the service outcome (i.e., AIGC outputs). Deploying OS^{2}A on ProSecutor, firstly, the MASP selection can be realized by sorting the reputation. Afterward, the contract theory is adopted to optimize the payment scheme and help clients avoid moral hazards in mobile networks. We implement the prototype of ProSecutor on BlockEmulator.Extensive experiments demonstrate that ProSecutor achieves 12.5x throughput and saves 67.5\% storage resources compared with BlockEmulator. Moreover, the effectiveness and efficiency of the proposed mechanisms are validated.
As the Internet of Vehicles (IoV) has become the critical part of Intelligent Vehicular Transportation Systems (IVTS), massive IoV entities (e.g., RSU, OBU, pedestrians’ mobile devices, etc.) get involved into IVTS. At present, one of the biggest challenges with IoV/IVTS is how to maintain a balance between security and privacy. The receivers need to be sure that they are receiving reliable messages from the origin and could trace or link the attacker’s identity, but the tracing or linking may work against the sender’s need for identity privacy. To solve the security and privacy problem, most of current works have proposed authentication solutions to provide anonymous, traceable and unlinkable schemes, which are still vulnerable to either Sybil attacks or quantum attacks. Therefore, we propose the blockchain-based post-quantum anonymous, traceable and linkable authentication scheme by utilizing NIST winner post-quantum algorithms and related post-quantum linkable ring signature. Grounded on the authentication scheme, we also develop key exchange mechanism, which help IoV entities perform efficient message authentication encryption/decryption during P2P communication and broadcast. The security analysis shows that our proposal is resistant to Sybil attack and provides other essential security characteristics including man-in-the-middle-proof and anti-replay. Finally, we perform detailed performance evaluation including each on-chain API execution time, the off-chain communication time and the on-board/on-chain storage requirements. To further evaluate the feasibility of our scheme in the IoV/IVTS environment, we also show the effectiveness of our proposal in a blockchain-based simulation study.
The Internet of Vehicles (IoV) is a crucial technology for Intelligent Transportation Systems (ITS) that integrates vehicles with the Internet and other entities. The emergence of 5 G and the forthcoming 6 G networks presents an enormous potential to transform the IoV by enabling ultra-reliable, low-latency, and high-bandwidth communications. Nevertheless, as connectivity expands, cybersecurity threats have become a significant concern. The issue has been further exacerbated by the rising number of zero-day (0-day) attacks, which can exploit unknown vulnerabilities and bypass existing Intrusion Detection Systems (IDSs). In this paper, we propose Zero-X, an innovative security framework that effectively detects both 0-day and N-day attacks. The framework achieves this by combining deep neural networks with Open-Set Recognition (OSR). Our approach introduces a novel scheme that uses blockchain technology to facilitate trusted and decentralized federated learning (FL) of the Zero-X framework. This scheme also prioritizes privacy preservation, enabling both CAVs and Security Operation Centers (SOCs) to contribute their unique knowledge while protecting the privacy of their sensitive data. To the best of our knowledge, this is the first work to leverage OSR in combination with privacy-preserving FL to identify both 0-day and N-day attacks in the realm of IoV. The in-depth experiments on two recent network traffic datasets show that the proposed framework achieved a high detection rate while minimizing the false positive rate. Comparison with related work showed that the Zero-X framework outperforms existing solutions.
Query exchange in the Social Internet of Vehicles (SIoV) can protect users’ trajectory information. However, this method lacks an appropriate incentive mechanism, which leads to cooperative users refusing to participate in query exchange. In order to provide cooperative users with incentives to participate in query exchange, this paper proposes a smart contract-based query exchange (SC-QE) trajectory privacy protection method. By creating a many-to-many smart contract, the method encourages the cooperative users to bid to the requesting users. Subsequently, in order to select a Best Similarity Deviation User (BSDU) for the requesting user to perform query exchange, the users in the smart contract are modeled as a weighted bipartite graph, and the matching between the requesting users and BSDUs is realized by means of a weighted bipartite graph best matching algorithm. Following successful verification of the query exchange transaction in the smart contract, the base station distributes rewards to the BSDU and uploads the query exchange transaction to the consortium blockchain. Experimental results show that compared with the deviation-based query exchange (DQE) method, the proposed method reduces the user processing time by 12% while increasing the continuous anonymous success rate by 29%. Therefore, the proposed method can reduce the service query time and improve the level of trajectory privacy protection.
Mishri Saleh Al-Marshoud, Mehmet Sabır Kiraz, Ali H. Al-Bayati
Vehicular Ad Hoc Networks (VANETs) are powerful platforms for vehicular data services and applications. The increasing number of vehicles has made the vehicular network diverse, dynamic, and large-scale, making it difficult to meet the 5G network’s demanding requirements. Decentralized systems are interesting and provide attractive services because they are publicly available (transparency), have an append-only ledger (robust integrity protection), remove single points of failure, and enable distributed key management and communication in a peer-to-peer network. Researchers dedicated substantial efforts to advancing vehicle communications, however conventional cryptographic mechanisms are insufficient which enabled us to look at decentralized technologies. Therefore, we revisit decentralized approaches with VANETs. Endpoint devices hold a wallet which may incorporate threshold key management methods like MPC wallets, HD Wallets, or multi-party threshold ECDSA/EdDSA/BLS. We also discuss trust management approaches and demonstrate how decentralization can improve integrity, security, privacy, and resilience to single points of failure. We also conduct a comprehensive review, comparing them with current requirements, and the latest authentication and secure communication architectures, which require the involvement of trusted but non-transparent authorities in certificate issuance/revocation. We highlight the limitations of these schemes from PKI deployment and recommend future research, particularly in the realm of quantum cryptography.
With the wide application of the Internet of Things (IoT), storing large amounts of IoT data and protecting data privacy has become a meaningful issue. In general, the access control mechanism is used to prevent illegal users from accessing private data. However, traditional data access control schemes face some non-ignorable problems, such as only supporting coarse-grained access control, the risk of centralization, and high trust issues. In this paper, an attribute-based data access control scheme using blockchain technology is proposed. To address these problems, attribute-based encryption (ABE) has become a promising solution for encrypted data access control. Firstly, we utilize blockchain technology to construct a decentralized access control scheme, which can grant data access with transparency and traceability. Furthermore, our scheme also guarantees the privacy of policies and attributes on the blockchain network. Secondly, we optimize an ABE scheme, which makes the size of system parameters smaller and improves the efficiency of algorithms. These optimizations enable our proposed scheme supports large attribute universe requirements in IoT environments. Thirdly, to prohibit attribute impersonation and attribute replay attacks, we design a challenge-response mechanism to verify the ownership of attributes. Finally, we evaluate the security and performance of the scheme. And comparisons with other related schemes show the advantages of our proposed scheme. Compared to existing schemes, our scheme has more comprehensive advantages, such as supporting a large universe, full security, expressive policy, and policy hiding.
Usama Arshad, Zahid Halim, Hisham Alasmary, Muhammad Waqas
Blockchain technology is used often as a merger with other technologies to achieve a high level of security, privacy, and robustness and to handle issues such as maliciousness of nodes, privacy leakage, the selfishness of nodes, communication delays, and high execution and transaction costs. There is currently a lack of a comprehensive system for automating and cost-effectively managing vehicle repairs, maintenance, and other associated services. To solve such issues we proposed a novel futuristic comprehensive model that integrates a blockchain-based framework to safely record vehicle maintenance, validate repair services, and oversee parts inventory. It employs smart contracts and consensus protocols to secure communications and data storage, thus reducing data breaches and vulnerabilities from single-point failures. A reward system is embedded within the network to encourage positive behavior and deter detrimental actions. We also incorporated advanced privacy-ensuring methods, like zero-knowledge proofs and secure multi-party computation, to safeguard sensitive data while preserving its utility. Our model features automatic detection and response mechanisms for node failure, improving network resilience by 25% thus also providing a 20% reduction in execution, operational costs, and scalability with an enhancement of 15%, underscoring the model’s efficiency in vehicular repair and maintenance activities. Results and simulations clearly depict the overall performance and efficiency in terms of security, privacy, node failure, and the management of vehicle repairs with respect to other closely related models.
In a multiparty signing protocol, also known as a threshold signature scheme, the private signing key is shared amongst a set of parties and only a quorum of those parties can generate a signature. Research on multiparty signing has been growing in popularity recently due to its application to cryptocurrencies. Most work has focused on reducing the number of rounds to two, and as a result: (a) are not fully simulatable in the sense of MPC real/ideal security definitions, and/or (b) are not secure under concurrent composition, and/or (c) utilize non-standard assumptions of different types in their proofs of security. In this paper, we describe a simple three-round multiparty protocol for Schnorr signatures that is secure for any number of corrupted parties; i.e., in the setting of a dishonest majority. The protocol is fully simulatable, secure under concurrent composition, and proven secure in the standard model or random-oracle model (depending on the instantiations of the commitment and zero-knowledge primitives). The protocol realizes an ideal Schnorr signing functionality with perfect security in the ideal commitment and zero-knowledge hybrid model (and thus the only assumptions needed are for realizing these functionalities). In our presentation, we do not assume that all parties begin with the message to be signed, the identities of the participating parties and a unique common session identifier, since this is often not the case in practice. Rather, the parties achieve consensus on these parameters as the protocol progresses.
Smart contracts are self-executing programs that can run on a blockchain. Due to the fact of being immutable after their deployment on blockchain, it is crucial to ensure their correctness. For this reason, various approaches for static analysis of smart contracts have been proposed, but they may be on the one hand imprecise or on the other hand difficult to train. In this paper, we propose a novel approach for detecting smart contract vulnerabilities using OpenAI's Generative Pre-trained Transformer 3 (GPT-3) language model. Our approach, called VulntHunt-GPT, uses GPT-3 to examine Ethereum smart contracts in order to identify the most popular vulnerabilities according to OWASP. We train VulntHunt-GPT on a dataset of smart contract functions and vulnerabilities to improve its accuracy. Our experiments show that VulntHunt-GPT outperforms almost all the existing state-of-the-art approaches in detecting a variety of vulnerabilities, including reentrancy attacks, integer overflow, and uninitialized storage. In addition, we conduct a case study to demonstrate the effectiveness of VulntHunt-GPT in detecting real-world smart contract vulnerabilities. We show that VulntHunt-GPT can identify previously unknown vulnerabilities in popular smart contracts, highlighting its potential for improving smart contract security. Our approach provides a promising direction for using natural language processing techniques to improve smart contract security and reduce the risk of smart contract exploits.
The promise of combining blockchain with artificial intelligence (AI) is compelling: auditable data provenance for training sets, tamper-evident logging for model lifecycle events, decentralized marketplaces for models and datasets, and automated enforcement of usage policies via smart contracts. Yet organizations quickly discover that operationalizing blockchain-based AI goes beyond stitching together two popular technologies. Differences in trust assumptions, latency and throughput profiles, security primitives, compliance expectations, and tooling maturity frequently collide at deployment time. This manuscript organizes those frictions into a coherent integration problem space and proposes a reference architecture and evaluation methodology to reason about trade-offs. We review the literature on blockchain consensus and scalability, privacy-preserving machine learning (federated learning, differential privacy, secure computation, and zero-knowledge proofs), data governance and compliance (e.g., GDPR), and MLOps platforms. We then present a methodology that stress-tests seven integration dimensions: architecture and partitioning (on-chain vs. off-chain responsibilities), performance and cost (latency, throughput, gas), privacy and confidentiality (leakage risks and mitigations), security and integrity (tamper-evidence, oracle trust), interoperability (heterogeneous chains and toolchains), compliance and governance (auditability versus erasure rights), and human/organizational fit (DevOps, incident response, and skills).
To govern smart contracts running on Ethereum, multiple Ethereum Request for Comment (ERC) standards have been developed, each containing a set of rules to guide the behaviors of smart contracts. Violating the ERC rules could cause serious security issues and financial loss, signifying the importance of verifying smart contracts follow ERCs. Today's practices of such verification are to either manually audit each single contract or use expert-developed, limited-scope program-analysis tools, both of which are far from being effective in identifying ERC rule violations. This paper presents a tool named AuditGPT that leverages large language models (LLMs) to automatically and comprehensively verify ERC rules against smart contracts. To build AuditGPT, we first conduct an empirical study on 222 ERC rules specified in four popular ERCs to understand their content, their security impacts, their specification in natural language, and their implementation in Solidity. Guided by the study, we construct AuditGPT by separating the large, complex auditing process into small, manageable tasks and design prompts specialized for each ERC rule type to enhance LLMs' auditing performance. In the evaluation, AuditGPT successfully pinpoints 418 ERC rule violations and only reports 18 false positives, showcasing its effectiveness and accuracy. Moreover, AuditGPT beats an auditing service provided by security experts in effectiveness, accuracy, and cost, demonstrating its advancement over state-of-the-art smart-contract auditing practices.
The blockchain-based electronic voting (e-voting) system, offers universities a safe, easy-to-use platform that enhances accuracy and integrity. Despite that, it is challenging to integrate the blockchain-based e-voting system with current platforms and private data. Managing latency is another requirement during the blockchain transactions (votes/elections). In this work, we suggested a novel system that uses smart contracts on the consortium blockchain to address these constraints. The voters and electors in a university can vote and elect respecting the rules established in smart contracts. The miners validate transactions using proof of work (PoW) and proof of stake (PoS). Data integrity and voter validity are ensured via the SHA-256 hash algorithm and the ECDSA signature. The implementation results demonstrate that the suggested method works better than the state-of-the-art. exceeds the state-of-the-art in terms of gas cost and execution time.
Francesco Buccafurri, Vincenzo De Angelis, Roberto Nardone
The interest in Self-Sovereign Identity (SSI) in research, industry, and governments is rapidly increasing. SSI is a paradigm where users hold their identity and credentials issued by authorized entities. SSI is revolutionizing the concept of digital identity enabling the definition of a trust framework wherein a service provider (verifier) validates the claims presented by a user (holder) for accessing services. However, current SSI solutions primarily focus on the presentation and verification of claims, overlooking a dual aspect: ensuring that the verifier is authorized to access the holder's claims. Addressing this gap, this paper introduces an innovative SSI-based solution that integrates decentralized wallets with Ciphertext-Policy Attribute-Based Proxy Re-Encryption (CP-ABPRE). This combination effectively addresses the challenge of verifier authorization. Our solution, implemented on the Ethereum platform, enhances accountability by notarizing key operations through a smart contract. The paper also offers a prototype demonstrating the practicality of the proposed approach. Furthermore, it provides an extensive evaluation of the solution's performance, emphasizing its feasibility and efficiency in real-world applications.
Abstract Today, the confidentiality and security of patient medical records is of great importance. This study proposes a scheme that aims to store, access, and share medical data without risking security vulnerabilities and attacks. In the proposed scheme, medical data are divided into sensitive and non-sensitive patient data. Three consensus mechanisms (Proof of Work (PoW), Proof of Stake (PoS), and Proof of Authority (PoA)) are implemented and compared to each other in terms of performance. The performance analysis of the proposed scheme shows that PoW provides approximately 21% and 9% better results than PoA and PoS for non-sensitive data in terms of block size, respectively. It also provides approximately 23% and 32% better results than PoA and PoS for sensitive data in terms of memory usage, respectively. The security analysis demonstrates that it has many security features and is strong against man-in-the-middle, impersonation, and modification attacks.
With the rapid growth of the Internet of Things (IoT), massive terminal devices are connected to the network, generating a large amount of IoT data. The reliable sharing of IoT data is crucial for fields such as smart home and healthcare, as it promotes the intelligence of the IoT and provides faster problem solutions. Traditional data sharing schemes usually rely on a trusted centralized server to achieve each attempted access from users to data, which faces serious challenges of a single point of failure, low reliability, and an opaque access process in current IoT environments. To address these disadvantages, we propose a secure and dynamic access control scheme for the IoT, named SDACS, which enables data owners to achieve decentralized and fine-grained access control in an auditable and reliable way. For access control, attribute-based control (ABAC), Hyperledger Fabric, and interplanetary file system (IPFS) were used, with four kinds of access control contracts deployed on blockchain to coordinate and implement access policies. Additionally, a lightweight, certificateless authentication protocol was proposed to minimize the disclosure of identity information and ensure the double-layer protection of data through secure off-chain identity authentication and message transmission. The experimental and theoretical analysis demonstrated that our scheme can maintain high throughput while achieving high security and stability in IoT data security sharing scenarios.
With the widespread integration of artificial intelligence (AI) and blockchain technologies, safeguarding privacy has become of paramount importance. These techniques not only ensure the confidentiality of individuals' data but also maintain the integrity and reliability of information. This study offers an introductory overview of AI and blockchain, highlighting their fusion and the subsequent emergence of privacy protection methodologies. It explores various application contexts, such as data encryption, de-identification, multi-tier distributed ledgers, and k-anonymity techniques. Moreover, the paper critically evaluates five essential dimensions of privacy protection systems within AI-blockchain integration: authorization management, access control, data security, network integrity, and scalability. Additionally, it conducts a comprehensive analysis of existing shortcomings, identifying their root causes and suggesting corresponding remedies. The study categorizes and synthesizes privacy protection methodologies based on AI-blockchain application contexts and technical frameworks. In conclusion, it outlines prospective avenues for the evolution of privacy protection technologies resulting from the integration of AI and blockchain, emphasizing the need to enhance efficiency and security for a more comprehensive safeguarding of privacy.
The metaverse, envisioned as the next digital frontier for avatar-based virtual interaction, involves high-performance models. In this dynamic environment, users' tasks frequently shift, requiring fast model personalization despite limited data. This evolution consumes extensive resources and requires vast data volumes. To address this, meta-learning emerges as an invaluable tool for metaverse users, with federated meta-learning (FML), offering even more tailored solutions owing to its adaptive capabilities. However, the metaverse is characterized by users heterogeneity with diverse data structures, varied tasks, and uneven sample sizes, potentially undermining global training outcomes due to statistical difference. Given this, an urgent need arises for smart coalition formation that accounts for these disparities. This paper introduces a dual game-theoretic framework for metaverse services involving meta-learners as workers to manage FML. A blockchain-based cooperative coalition formation game is crafted, grounded on a reputation metric, user similarity, and incentives. We also introduce a novel reputation system based on users' historical contributions and potential contributions to present tasks, leveraging correlations between past and new tasks. Finally, a Stackelberg game-based incentive mechanism is presented to attract reliable workers to participate in meta-learning, minimizing users' energy costs, increasing payoffs, boosting FML efficacy, and improving metaverse utility. Results show that our dual game framework outperforms best-effort, random, and non-uniform clustering schemes - improving training performance by up to 10%, cutting completion times by as much as 30%, enhancing metaverse utility by more than 25%, and offering up to 5% boost in training efficiency over non-blockchain systems, effectively countering misbehaving users.
Today, we live in an era where data is invaluable, and an incomprehensible amount of data is created daily.The companies that have gained a competitive edge are the ones that are already embracing business intelligence and data utilization, no matter the industry.In the case of Google, users' data has helped ensure customized services.The collaboration and sharing of these data among enterprises have emerged as a significant privacy and economic concern.The concerns include the risk of data breaches, legal consequences, and lack of incentive mechanisms for Data Subjects.Using Google Inc. as a case study, this paper will address privacy issues in data-sharing using blockchain technology-Hyperledger Fabric.Hyperledger Fabric is an open-source enterprise-grade permissioned distributed ledger technology (DLT) platform designed for use in enterprise contexts that delivers some key differentiating capabilities over other popular distributed ledger or blockchain platforms [1].This solution will ensure that user data collected by Google Inc. based on the agreed privacy and data-sharing policy between the user and the company is adhered to in a permissioned and trusted environment.Users will have the advantage of data privacy and compensation through digital tokens as they share their data with interested data consumers, thus becoming active actors that stand to profit from the data-driven economy.The Data Subject is an end user whose personal data can be collected.In this paper,
Decentralized Federated Learning (FL) offers a paradigm where independent entities collaboratively train a machine learning model while preserving the privacy of their datasets. Integrating blockchain technology into decentralized FL frameworks is critical to establishing the trust necessary for user participation. However, existing FL systems using blockchain often struggle with scalability, latency, and privacy issues, particularly in permissionless blockchain contexts. This paper proposes OpenFL, a novel, collateral-backed reputation system implemented on the Ethereum blockchain. This system aims to foster trust among participants in a decentralized FL environment. We present a fully autonomous smart contract platform specifically tailored to facilitate FL processes among anonymous users. Furthermore, we address potential security concerns by detailing our strategies to mitigate various attack vectors. To validate our system’s efficacy, we conducted experiments on the Ethereum Ropsten testnet using the MNIST and CIFAR-10 datasets. Our findings demonstrate OpenFL’s capability to overcome the inherent limitations of permissionless blockchains while highlighting the significance of open-access protocols in this context. OpenFL can potentially broaden the participant base in trust-sensitive applications by reducing entry barriers, thus substantially contributing to decentralized machine learning.