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).
Given the importance of distributed intelligence systems in modern computers, their ethical implications must be examined. The innovative Ethical-Aware Distributed Intelligence Framework (EADI) tackles ethical issues in decentralized decision-making. EADI's robust algorithms protect privacy, advance accountability, and decrease prejudices. Distributed intelligence becomes more accountable and fairer. The revolutionary Privacy-keeping Distributed Learning Algorithm (PPDLA) from EADI protects data inputs and privacy during collaborative training using noise. This method improves privacy over linear regression and support vector machines. The transparent and distributed ledger created by the Decentralized accountability Record Algorithm (DALA) simplifies monitoring and enforcing accountability. EADI beats decision trees and k-nearest neighbors, even with different feedback metrics. The Fairness-Aware Decentralized Decision Algorithm (FADDA) designed by EADI mitigates decision-making shortcomings to provide fair results. EADI surpasses existing techniques in various sectors to create and execute ethical distributed intelligence. This makes intelligent decision-making clearer and more reliable.
Vehicular Ad-hoc Networks (VANETs) hold promise for traffic management and driver safety, but security and privacy challenges persist. Existing protocols face limitations in key management and verification time costs. An efficient blockchain-based conditional privacy-preserving authentication mechanism leverages PKI and the functionality of Ethereum. The protocol optimizes certificate management, reduces storage requirements, and supports batch verification, improving traceability and verification efficiency. Evaluation of test networks and VANET simulations demonstrate the proposed algorithm’s superiority over existing algorithms. Although the existing solutions handle traceability time costs and verification costs efficiently. Further improvement in the efficiency of existing state-of-art models can be achieved by encoding all functions before hashing. This proposed approach tends to be more gas-efficient than previous work, particularly in dynamic data structure scenarios. Gas efficiency improvements are often more noticeable in VANET scenarios where terabytes of messages are communicated by vehicles daily, and this can also reduce the size of Signature of Knowledge (SoK) proof by using compression and parameter tuning techniques.
In this chapter, we embark on a journey through the dynamic intersection of blockchain technology and federated machine learning (FML). This chapter elucidates the pivotal role of FML in mitigating data privacy concerns in the ever-expanding field of Artificial Intelligence. This chapter begins by unveiling the intrinsic challenges stemming from centralized data collection and traditional machine learning (ML) methods. It emphasizes the urgent need for innovative solutions that not only enhance learning efficiency but also ensure the confidentiality and security of sensitive data. FML emerges as a promising paradigm where disparate parties collaboratively train ML models without the necessity of centralized data aggregation. This chapter underscores the merits of FML, which encompass data privacy preservation and efficient model training. However, it doesn&s;t shy away from exposing the stumbling blocks encountered, including centralization issues, potential adversarial updates, and the overarching data privacy dilemma. To address these challenges, this chapter introduces blockchain technology as a robust foundation. Ethereum and smart contracts (SCs) take center stage as they empower a decentralized framework for federated learning. An SC assumes the role of a coordinator, ensuring the secure aggregation of model updates. Furthermore, the innovative application of zero-knowledge STARK proofs is employed to verify the legitimacy of client training processes, thus upholding the integrity of data. This chapter concludes by evaluating the performance of this blockchain-facilitated federated learning model, showcasing promising results that are in close proximity to theoretical limits. However, it also highlights trade-offs, including increased time requirements and gas costs associated with blockchain integration. In sum, this chapter sets the stage for an exciting exploration of how blockchain technology can empower federated learning, transforming it into a robust, secure, and efficient approach to ML while safeguarding data privacy. It opens the door to further research and innovation in this rapidly evolving and highly relevant field.
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.
As a critical infrastructure for contemporary information technology industry, industrial internet of things (IIoT) contains a vast amount of sensitive data, making it a key requirement to ensure data security. As the use of wireless networks as a means of communication between nodes is becoming more and more common, in order to prevent malicious attacks from compromising the system, a zero-trust authentication system is necessary. In this article, we propose a comprehensive implementation framework for zero-trust verification of IIoT wireless transmission nodes, which utilizes federated learning to achieve zero-trust rule training and terminal model training, while employing blockchain technology for on-chain aggregation and cloud backup of the models. This approach enhances the accuracy and availability of the zero-trust rules while safeguarding the security of IIoT nodes. The constructed zero-trust framework incorporates a self-incremental learning function, and experiments show that it achieves a high level of accuracy at recognising attacks. Finally, we discuss the challenges of utilizing federated learning in zero-trust for IIoT and several potential solutions to address these challenges.
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.
Akash Bag, Paridhi Sharma, Pranjal Khare, Souvik Roy
Our personal information, or “digital footprint,” is gathered and used in today's digital age. Digital footprints are kept, unlike snow footprints. There is a large market for this data, which businesses utilize to analyze consumer preferences. Businesses collecting a lot of data in one place pose a privacy risk. Thus, people are worried. Businesses prefer not to utilize intermediaries to manage client data to save money. Therefore, new technology is needed to make online interactions safer and more efficient. We're considering “distributed ledger technology.” This technology is interesting because it securely collects, stores, and processes data without central authority. It has data immutability, transparency, and safety. A problem exists. The European GDPR (general data protection regulation) may conflict with this technology. This chapter will examine this tension, focusing on the right to be forgotten, which permits people to delete their data. It will examine how this new technology and existing privacy policies can function together or need tweaking.
Decentralized Identifiers have recently expanded into Internet of Things devices and are crucial in securing users' digital identities and data. However, Decentralized Identifiers face challenges in scenarios necessitating authority delegation and anonymity, such as when dealing with legal guardianship for minors, device loss or damage, and specific medical contexts involving patient information. This paper aims to strengthen data sovereignty within the Decentralized Identifier system by implementing a secure authority delegation and anonymity scheme. It suggests optimizing verifiable presentations by utilizing a sequential aggregate signature, a Non-Interactive Zero-Knowledge Proof, and a Merkle tree to prevent against linkage and Sybil attacks while facilitating delegation. This strategy mitigates security risks related to delegation and anonymity, efficiently reduces the computational and verification efforts for signatures, and reduces the size of verifiable presentations by about 1.2 to 2 times.
A ride-sharing system provides many advantages. It reduces energy consumption. Moreover, it mitigates traffic congestion. Furthermore, it provides benefits to drivers and passengers in that they share travel costs (gas, toll ticket). Ride-sharing has been getting very popular since the COVID-19 pandemic. Since COVID-19 is very contagious and spread by infected people via coughs, sneezes, or talking, people avoid using public transportation to get the disease. People prefer to travel with a few people to protect their health. To prevent people from getting the disease and to have the advantages of a ride-sharing system, we introduce a ride-sharing system in which a driver only travels with a passenger for each event. Traveling with only one person provides advantages to both the driver and passenger. The driver (passenger) not only shares the cost of the trip but also reduces the risk of getting or spreading the disease. This paper proposes a smart-contract-based ride-sharing system that uses a $Vickrey$ (second highest bid) auction mechanism during a pandemic to determine the passenger. The system provides a decentralized, transparent, trackable, verifiable, and secure ride-sharing with the help of consortium blockchain. Using smart contracts in the system allows users’ bids to be transparent and verifiable. Furthermore, the proposed system provides a secure and lightweight mechanism to protect users' (drivers and passengers) travel data (locations and cost of travel). Their travel data for each event is going to be unlinkable.
While centralized servers pose a risk of being a single point of failure, decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Merging distributed computing with cryptographic techniques, decentralized technologies introduce a novel computing paradigm. Blockchain ensures secure, transparent, and tamper-proof data management by validating and recording transactions via consensus across network nodes. Federated Learning (FL), as a distributed machine learning framework, enables participants to collaboratively train models while safeguarding data privacy by avoiding direct raw data exchange. Despite the growing interest in decentralized methods, their application in FL remains underexplored. This paper presents a thorough investigation into Blockchain-based FL (BCFL), spotlighting the synergy between blockchain's security features and FL's privacy-preserving model training capabilities. First, we present the taxonomy of BCFL from three aspects, including decentralized, separate networks, and reputation-based architectures. Then, we summarize the general architecture of BCFL systems, providing a comprehensive perspective on FL architectures informed by blockchain. Afterward, we analyze the application of BCFL in healthcare, IoT, and other privacy-sensitive areas. Finally, we identify future research directions of BCFL.
To address the challenge of balancing privacy protection with regulatory oversight in blockchain transactions, we propose a regulatable privacy protection scheme for blockchain transactions. Our scheme utilizes probabilistic public-key encryption to obscure the true identities of blockchain transaction participants. By integrating commitment schemes and zero-knowledge proof techniques with deep learning graph neural network technology, it provides privacy protection and regulatory analysis of blockchain transaction data. This approach not only prevents the leakage of sensitive transaction information, but also achieves regulatory capabilities at both macro and micro levels, ensuring the verification of the legality of transactions. By adopting an identity-based encryption system, regulatory bodies can conduct personalized supervision of blockchain transactions without storing users’ actual identities and key data, significantly reducing storage computation and key management burdens. Our scheme is independent of any particular consensus mechanism and can be applied to current blockchain technologies. Simulation experiments and complexity analysis demonstrate the practicality of the scheme.
The rapid development of the Hospital Information System has significantly enhanced the convenience of medical research and the management of medical information. However, the internal misuse and privacy leakage of medical big data are critical issues that need to be addressed in the process of medical research and information management. Access control serves as a method to prevent data misuse and privacy leakage. Nevertheless, traditional access control methods, limited by their single usage scenario and susceptibility to single point failures, fail to adapt to the polymorphic, real-time, and sensitive characteristics of medical big data scenarios. This paper proposes a smart contracts and risk-based access control model (SCR-BAC). This model integrates smart contracts with traditional risk-based access control and deploys risk-based access control policies in the form of smart contracts into the blockchain, thereby ensuring the protection of medical data. The model categorizes risk into historical and current risk, quantifies the historical risk based on the time decay factor and the doctor's historical behavior, and updates the doctor's composite risk value in real time. The access control policy, based on the comprehensive risk, is deployed into the blockchain in the form of a smart contract. The distributed nature of the blockchain is utilized to automatically enforce access control, thereby resolving the issue of single point failures. Simulation experiments demonstrate that the access control model proposed in this paper effectively curbs the access behavior of malicious doctors to a certain extent and imposes a limiting effect on the internal abuse and privacy leakage of medical big data.
Open access
Blockchain Technology Applications and Security
Privacy-Preserving Technologies in Data
Artificial Intelligence in Healthcare and Education
S. B. Goyal, Anand Singh Rajawat, Manoj Kumar, Prerna Agarwal
INTRODUCTION: Cloud computing's offshoot, fog computing, moves crucial data storage, processing, and networking capabilities closer to the people who need them. There are certain advantages, such improved efficiency and lower latency, but there are also some major privacy and security concerns. For these reasons, this article presents a new paradigm for fog computing that makes use of blockchain and Artificial Intelligence (AI). OBJECTIVES: The main goal of this research is to create and assess a thorough framework for fog computing that incorporates AI and blockchain technology. With an emphasis on protecting the privacy and integrity of data transactions and streamlining the management of massive amounts of data, this project seeks to improve the security and privacy of Industrial Internet of Things (IIoT) systems that are cloud-based. METHODS: Social network analysis methods are utilised in this study. The efficiency and accuracy of data processing in fog computing are guaranteed by the application of artificial intelligence, most especially Support Vector Machine (SVM), due to its resilience in classification and regression tasks. The network's security and reliability are enhanced by incorporating blockchain technology, which creates a decentralised system that is tamper resistant. To make users' data more private, zero-knowledge proof techniques are used to confirm ownership of data without actually disclosing it. RESULTS: When applied to fog computing data, the suggested approach achieves a remarkable classification accuracy of 99.8 percent. While the consensus decision-making process of the blockchain guarantees trustworthy and secure operations, the support vector machine (SVM) efficiently handles massive data analyses. Even in delicate situations, the zero-knowledge proof techniques manage to keep data private. When these technologies are integrated into the fog computing ecosystem, the chances of data breaches and illegal access are greatly reduced. CONCLUSION: Fog computing, which combines AI with blockchain, offers a powerful answer to the privacy and security issues with cloud centric IIoT systems. Combining SVM with AI makes data processing more efficient, while blockchain's decentralised and immutable properties make it a strong security measure. Additional security for user privacy is provided via zero-knowledge proofs. Improving the privacy and security of fog computing networks has never been easier than with this novel method.