AI requires heavy amounts of storage and compute with assets that are commonly stored in AI Hubs. AI Hubs have contributed significantly to the democratization of AI. However, existing implementations are associated with certain benefits and limitations that stem from the underlying infrastructure and governance systems with which they are built. These limitations include high costs, lack of monetization and reward, lack of control and difficulty of reproducibility. In the current work, we explore the potential of decentralized technologies - such as Web3 wallets, peer-to-peer marketplaces, storage and compute, and DAOs - to address some of these issues. We suggest that these infrastructural components can be used in combination in the design and construction of decentralized AI Hubs.
Nathanaël Denis, Maryline Laurent, Sophie Chabridon
The Internet of Things (IoT) brings new ways to collect privacy-sensitive data from billions of devices. Well-tailored distributed ledger technologies (DLTs) can provide high transaction processing capacities to IoT devices in a decentralized fashion. However, privacy aspects are often neglected or unsatisfying, with a focus mainly on performance and security. In this article, we introduce decentralized usage control mechanisms to empower IoT devices to control the data they generate. Usage control defines obligations, i.e., actions to be fulfilled to be granted access, and conditions on the system in addition to data dissemination control. The originality of this article is to consider the usage control system as a component of distributed ledger networks, instead of an external tool. With this integration, both technologies work in synergy, benefiting their privacy, security, and performance. We evaluated the performance improvements of integration using the IOTA technology, particularly suitable due to the participation of small devices in the consensus. The results of the tests on a private network show an approximate 90% decrease of the time needed for the usage control system to push a transaction and make its access decision in the integrated setting, regardless of the number of nodes in the network.
Bitcoin-NG is an extensible blockchain protocol based on the same trust model as Bitcoin. It divides each epoch into one Key-Block and multiple Micro-Blocks, effectively improving transaction processing capacity. Bitcoin-NG adopts a special incentive mechanism (i.e., the transaction fees in each epoch are split to the current and next leader) to maintain its security. However, there are some limitations to the existing incentive analysis of Bitcoin-NG in recent works. First, the incentive division method of Bitcoin-NG only includes some specific mining attack strategies of adversary, while ignoring more stubborn attack strategies. Second, once adversaries find a whale transaction, they will deviate from honest mining strategy to obtain extra reward. In this paper, we are committed to solving these two limitations. First, we propose a novel mining strategy named Greedy-Mine attack. Then, we formulate a Markov Decision Process (MDP) model to analyze the competition of honest miners and adversaries. Furthermore, we analysis the extra reward of adversaries and summarize the mining power proportion range required for malicious adversaries to launch Greedy-Mine to obtain extra returns. Finally, we make a backward-compatibility progressive modification to Bitcoin-NG protocol that would raise the threshold of propagation factor from 0 to 1. Meanwhile, we get the winning condition of adversaries when adopting Greedy-Mine, compared with honest mining. Simulation and experimental results indicate that Bitcoin-NG is not incentive compatible, which is vulnerable to Greedy-Mine attack.
The increased demand for data availability in every industry is driving individuals to exchange and store data on centralized platforms such as clouds so that the intended audience may access it. To facilitate data exchange and storage in the medical industry, organizations and patients are building cloud platforms. However, the most pressing issue that everyone faces is data protection and security. Here, we describe many techniques that are available to protect the system and meet the requirement for data privacy preservation in the medical industry. Some algorithms are Zero-Knowledge Proof, Principal Component Analysis and Random Projection, Generative Adversarial Networks, blockchain and cloud computing, Quasi-Identifier Recognition, Q-learning Neural Network, digital signature, and others.
Yunhua He, Mingshun Luo, Bin Wu, Limin Sun · 7 authors
With the digital transformation of the energy industry, energy blockchain is playing an important role in application areas, such as energy data sharing and distributed power trading. In this process, the use of energy data is a top priority. Federated learning (FL) can enable the analysis and computation of energy data while protecting their privacy. However, traditional FL relies on a central server and parties involved are not fully trusted. In energy blockchain environment, FL also faces data poisoning attacks launched by energy departments, besides, the supervisory committee carrying out checking models can launch deception attacks. Therefore, we propose a game theory-based incentive mechanism for collaborative security of FL in energy blockchain environment, which can discourage nodes from taking malicious behaviors in iterative training of FL. First, we propose an FL model in energy blockchain environment, which can protect privacy and achieve collaborative security. Considering that game theory can be used to analyze the strategies of participants, we build a game model with energy departments and supervisory committee as players and design our incentive mechanism based on game theory, which is implemented by smart contracts. Even if the accuracy of model checking algorithm is low, malicious behaviors in FL can be reduced by using our incentive mechanism. In particular, we prove that our mechanism can lead game model to a Nash equilibrium (NE) that achieve collaborative security. Security analysis and experimental evaluation show that our incentive mechanism is feasible in energy blockchain with robustness, reliability, and low complexity.
With the widespread popularity of the Internet of Things and various intelligent medical devices, the amount of medical data is rising sharply, and thus medical data processing has become increasingly challenging. Mobile edge computing technology allows computing power to be allocated at the edge closer to users, which enables efficient data offloading for healthcare systems. However, existing studies on medical data offloading seldom guarantee effective data privacy and security. Moreover, the research equipping data offloading architectures with Blockchain neglect the delay and energy consumption costs incurred in using Blockchain technology for medical data offloading. Therefore, in this paper, we propose a data offloading scheme for healthcare based on Blockchain technology, which achieves optimal medical resource allocation and simultaneously minimizes the cost of offloading tasks. Specifically, we design a smart contract to ensure secure data offloading. And, we formulate the cost problem as a Markov Decision Process, solved by a policy search-based deep reinforcement learning (Asynchronous Advantage Actor-Critic) scheme, where we jointly consider offloading decisions, allocation of computing resources and radio transmission bandwidth, and Blockchain data security audits. The security of our smart-contract-based mechanism is theoretically and empirically proved, while extensive experimental results also show that our solution can obtain superior performance gains with lower cost than other baselines.
Muhammad Umar Majigi, Ismaila Idris, Shafi’i Muhammad Abdulhamid, Andrew A. Uduimoh
The possibility of implementing advanced applications, such as improved driving safety, has increased with the rapid development of vehicular telematics, and existing vehicular services have been enriched through data sharing and analysis between vehicles. This research uses smart contracts and consortium blockchain zero knowledge proof to secure data sharing and storage in vehicular networks. The results indicate that, for message sizes (m), both data_ experiments _2 and 1 produce ciphertext of the same size 157 bits, with the exception of 'gnfuv-temp-exp1-55d487b85b-5g2xh,' which generates ciphertext of 156 bits with the lowest decryption time of 26,865ms and a small decrease in encryption time between 28,620ms and 28,162ms. the proposed model validation shows that the model performed better than the Advanced encryption standard in terms of ciphertext size, encryption time and decryption time in comparison and it satisfies the good and robust blockchain-based zero knowledge proof model for secure data sharing and storage for distributed VANET. The scheme achieves high levels of security while operating with reasonable efficiency, reliability and availability according to numerical results.
To avoid the security vulnerabilities and privacy leakage risks in the process of government data sharing, a data sharing scheme for government data based on blockchain is proposed in this paper. As one of the basic protocols for the next generation of global credit authentication and value Internet, blockchain technology has been paid more and more attention. The blockchain is used to realize data tamper-proof, distributed storage, privacy protection, traceability, and access control. Data sharing and exchange agreements between government public services have adopted blockchain technology, providing identity authentication and message permission management. Using the blockchain to record the sharing behavior and provide traceability, the smart contract is used to share and manage the data and ensure the data’s consistency on-chain and off-chain. Due to shortcomings of government data sharing, the paper designs a data-sharing scheme and technical framework based on blockchain technology. From its perspective, it takes housing provident fund data sharing, for example, to establish a provident fund chain in the industry. This blockchain application scenario has been applied to the housing provident fund industry, forming a new model of industry data sharing.
Digital healthcare services have become an integral part of our lives. There is an increasing number of healthcare professionals and patients using medical wearables for diagnosis and treatment, which simplifies and improves the diagnostic and therapeutic process. However, inappropriate use of medical data may result in the disclosure of private patient information. For protecting patients' privacy when using medical wearables, we propose a new blockchain-based data access security scheme. Specifically, the elliptic curve encryption algorithm and zero-knowledge authentication method are used to authenticate the identity of patients and doctors in the blockchain network. Furthermore, we develop a smart recommendation method based on deep reinforcement learning to recommend appropriate doctors for patients. Next, patients allow recommended doctors to access their medical data, and smart contracts specifically designed for secure data access to medical wearables will regulate subsequent data access. The security analysis and experimental results demonstrate that the proposed scheme can effectively protect patients' privacy during treatment through secure authentication and data access for medical wearables.
With cloud-hosted web applications becoming ubiquitous, the security risks presented for user personal data that is migrated to the cloud are at an all-time high. When using a cloud-hosted web application, users only ever interact with web interfaces of the web applications and are usually completely unaware of how their data is distributed amongst the multiple cloud service providers that the web application uses, making it difficult to verify the lawful use and ownership of personal data. The General Data Protection Regulation (GDPR) seeks to empower users to gain better control over their personal data. Blockchain-based approaches have risen in popularity over the recent years to tackle the challenge of verifying GDPR compliance in multi-cloud environments. By deploying smart contracts on the blockchain, we can create transparent and immutable logs of data processes in the hopes of automating GDPR compliance verification. However, the existing works are still limited to provide a user-centric compliance verification. To this end, we propose a user-centric, blockchain-based framework for data management in a cloud environment where all GDPR-relevant data operations take place on the blockchain through well-defined smart contracts.
Privacy by design suggests seven principles to embed privacy into systems, but artificial intelligence (AI) and data practice shows a ‘privacy paradox’ in the behaviour of users. The ever-evolving AI capabilities outpace privacy policies and people require more insight into the possible use of their data to make informed consent decisions. Keeping the human in the loop for reconsenting brings huge efforts and is disproportional to agile development. Decentral data management approaches promise efficient ways to handle crowd ownership, rights and their governance involvement, supporting privacy-by-design. This paper illustrates how a DataUnion approach for data-centric AI can efficiently combine decentralised, privacy-preserving features such as data non-fungible tokens (NFTs), federated learning, marketplaces, provenance or value sharing. The discussion concludes on data provenance being a key lever leading to the need for a blockchain protocol that makes privacy contributions an incentivised asset along the data value chain of enriching, verifying and governing data for AI. The quest for explainability in a model-centric approach is the quest for provenance in a data-centric approach.
Federated learning is a decentralized machine learning paradigm that allows multiple clients to collaborate by leveraging local computational power and the model’s transmission. This method reduces the costs and privacy concerns associated with centralized machine learning methods while ensuring data privacy by distributing training data across heterogeneous devices. On the other hand, federated learning has the drawback of data leakage due to the lack of privacy-preserving mechanisms employed during storage, transfer, and sharing, thus posing significant risks to data owners and suppliers. Blockchain technology has emerged as a promising technology for offering secure data-sharing platforms in federated learning, especially in Industrial Internet of Things (IIoT) settings. This survey aims to compare the performance and security of various data privacy mechanisms adopted in blockchain-based federated learning architectures. We conduct a systematic review of existing literature on secure data-sharing platforms for federated learning provided by blockchain technology, providing an in-depth overview of blockchain-based federated learning, its essential components, and discussing its principles, and potential applications. The primary contribution of this survey paper is to identify critical research questions and propose potential directions for future research in blockchain-based federated learning.
Artificial Intelligence Generated Content (AIGC) is one of the latest achievements in AI development. The content generated by related applications, such as text, images and audio, has sparked a heated discussion. Various derived AIGC applications are also gradually entering all walks of life, bringing unimaginable impact to people's daily lives. However, the rapid development of such generative tools has also raised concerns about privacy and security issues, and even copyright issues in AIGC. We note that advanced technologies such as blockchain and privacy computing can be combined with AIGC tools, but no work has yet been done to investigate their relevance and prospect in a systematic and detailed way. Therefore it is necessary to investigate how they can be used to protect the privacy and security of data in AIGC by fully exploring the aforementioned technologies. In this paper, we first systematically review the concept, classification and underlying technologies of AIGC. Then, we discuss the privacy and security challenges faced by AIGC from multiple perspectives and purposefully list the countermeasures that currently exist. We hope our survey will help researchers and industry to build a more secure and robust AIGC system.
Roshan Singh, Debanjan Roy Chowdhury, Sukumar Nandi, Sunit Kumar Nandi
Edge computing is the paradigm that offers low latency services by bringing computation and data storage closer to data sources. Due to the resource-constrained nature of the edge devices, they may not be able to handle all the computation tasks independently. In such cases, task offloading to the peer edge devices is looked for. However, as the devices are from different manufacturers, collaboration among them is challenging due to the lack of any trusted common platform. Blockchain along with smart contracts can help bridge the gap by providing a common decentralized platform of mutual trust. In this work, we introduce a permissioned blockchain-based platform for decentralized computation task offloading among edge devices. A smart contract is designed which provides an ecosystem of mutual trust by ensuring rewards for the honest participants and punishment for the misbehaving participants. Our proposed scheme also does fractional task offloading to encourage the participation of resource-constrained devices. We evaluated our scheme through a proof of concept implementation over an Ethereum-based testbed and found that our scheme can provide a trust-worthy eco-system for resource-constrained edge devices.
This paper analyses the challenges of balancing anonymity, utility and security in financial services. It argues that the traditional approach of using clearinghouses to enhance utility has come at the expense of anonymity. However, the advent of privacy-enhancing technologies like zero-knowledge proofs and federated AI has begun to minimise these trade-offs. The paper provides a case study of Merit Protocol, a company that is using these technologies to address the problem of predatory payday loans. Merit Protocol’s platform allows employers to pre-underwrite loans for their employees without sharing sensitive data. This approach empowers employers to support their employees’ financial needs while maintaining privacy and reducing dependency on traditional credit agencies. The paper concludes by discussing the challenges that the financial services industry must address in order to fully realise the potential of privacy-enhancing technologies. These challenges include navigating legacy compliance frameworks and improving the ease of use of these technologies. Readers can expect to gain a deeper understanding of the challenges of balancing anonymity, utility and security in financial services. They will also learn about the potential of privacy-enhancing technologies to address these challenges.
Federated learning (FL) is a technique that involves multiple participants who update their local models with private data and aggregate these models using a central server. Unfortunately, central servers are prone to single-point failures during the aggregation process, which leads to data leakage and other problems. Although many studies have shown that a blockchain can solve the single-point failure of servers, blockchains cannot identify or mitigate the effect of backdoor attacks. Therefore, this paper proposes a blockchain-based FL framework for defense against backdoor attacks. The framework utilizes blockchains to record transactions in an immutable distributed ledger network and enables decentralized FL. Furthermore, by incorporating the reverse layer-wise relevance (RLR) aggregation strategy into the participant’s aggregation algorithm and adding gradient noise to limit the effectiveness of backdoor attacks, the accuracy of backdoor attacks is substantially reduced. Furthermore, we designed a new proof-of-stake mechanism that considers the historical stakes of participants and the accuracy for selecting the miners of the local model, thereby reducing the stake rewards of malicious participants and motivating them to upload honest model parameters. Our simulation results confirm that, for 10% of malicious participants, the success rate of backdoor injection is reduced by nearly 90% compared to Vanilla FL, and the stake income of malicious devices is the lowest.
The Metaverse is established by twining a practical world in a virtual form, where users could become creators of learning-based user-generated content (UGC). However, the digital assets are not owned by the creators, which deviates from the decentralized Metaverse based on blockchain in Web 3.0. The contribution incentive design for UGC still faces the challenges of ownership verification and privacy protection. To this end, we propose minting the learning model into the non-fungible token (NFT) with federated learning (FL) assistance (referred to as FL-NFT), such that users as stakeholders can control the ownership and share the economic value of UGC. Specifically, the users are encouraged to establish a decentralized autonomous organization (DAO) to aggregate local models and mint FL-NFT. We formulate an auction interaction of FL-NFT as imperfect information Stackelberg game (IISG) to optimize the bidding strategies to realize individual rationality. Finally, we conduct simulations to show the effectiveness of the proposed scheme.
In this paper, we present a novel method for timestamping and data notarisation on a distributed ledger. The problem with on-chain hashes is that a cryptographic hash is a deterministic function that it allows the blockchain be used as an oracle that confirms whether potentially leaked data is authentic (timestamped or notarised by the user). Instead, we suggest using on-chain Pedersen commitments and off-chain zero-knowledge proofs (ZKP) for designated verifiers to prove the link between the data and the on-chain commitment. Our technique maintains the privacy of the data, and retains control of who can access it and when they can access it. This holds true even on a public blockchain, and even if the data is leaked by authorised parties. Indeed, an authorised data consumer (a designated-verifier for the ZKP), who discloses the data publicly, cannot convince anyone about the legitimacy of the data (in the sense that it is consistent with the information uploaded to the blockchain), because the ZKP proof is valid only for them. Our techniques can be used in scenarios where it is required to audit highly-sensitive data (e.g. application logs) by specific third parties, or to provide on-demand data certification by notaries.
The Internet of Things (IoT) compromises multiple devices connected via a network to perform numerous activities. The large amounts of raw user data handled by IoT operations have driven researchers and developers to provide guards against any malicious threats. Blockchain is a technology that can give connected nodes means of security, transparency, and distribution. IoT devices could guarantee data centralization and availability with shared ledger technology. Federated learning (FL) is a new type of decentralized machine learning (DML) where clients collaborate to train a model and share it privately with an aggregator node. The integration of Blockchain and FL enabled researchers to apply numerous techniques to hide the shared training parameters and protect their privacy. This study explores the application of this integration in different IoT environments, collectively referred to as the Internet of X (IoX). In this paper, we present a state-of-the-art review of federated learning and Blockchain and how they have been used in collaboration in the IoT ecosystem. We also review the existing security and privacy challenges that face the integration of federated learning and Blockchain in the distributed IoT environment. Furthermore, we discuss existing solutions for security and privacy by categorizing them based on the nature of the privacy-preservation mechanism. We believe that our paper will serve as a key reference for researchers interested in improving solutions based on mixing Blockchain and federated learning in the IoT environment while preserving privacy.
Machine learning, particularly using neural networks, is now widely adopted in practice even with the IoT paradigm; however, training neural networks at the edge, on IoT devices, remains elusive, mainly due to computational requirements. Furthermore, effective training requires large quantities of data and privacy concerns restrict accessible data. Therefore, in this paper, we propose a method leveraging a blockchain and federated learning to train neural networks at the edge effectively bypassing these issues and providing additional benefits such as distributing training across multiple devices. Federated learning trains networks without storing any data and aggregates multiple networks, trained on unique data, forming a global network via a centralized server. By leveraging the decentralized nature of a blockchain, this centralized server is replaced by a P2P network, removing the need for a trusted centralized server and enabling the learning process to be distributed across participating devices. Our results show that networks trained in such a manner have negligible differences in accuracy compared to traditionally trained networks on IoT devices and are less prone to overfitting. We conclude that not only is this a viable alternative to traditional paradigms but is an improvement that contains a wealth of benefits in an ecosystem such as a hospital.
As the high-mobility nature of the vehicles results in frequent leaving and joining the transportation network, real-time data must be collected and shared in a timely manner. In such a transportation network, malicious vehicles can disrupt services and create serious issues, such as deadlocks and accidents. The blockchain is a technology that ensures traceability, consistency, and security in transportation networks. In this study, we integrated edge computing and blockchain technology to improve the optimal utilization of resources, especially in terms of computing, communication, security, and storage. We propose a novel, edge-integrated, blockchain-based vehicle platoon security scheme. For the vehicle platoon, we developed the security architecture, implemented smart contracts for practical network scenarios in network simulator version 3, and integrated them with the simulation urban mobility traffic control interface API. We exhaustively simulated all the scenarios and analyzed the communication performance metrics, such as throughput, delay, and jitter, and the security performance metrics, such as mean squared error, communication, and computational cost. The performance results demonstrate that the developed scheme can solve security-related issues more effectively and efficiently in smart cities.
Lioba Heimbach, Lucianna Kiffer, Christof Ferreira Torres, Roger Wattenhofer
With Ethereum's transition from Proof-of-Work to Proof-of-Stake in September 2022 came another paradigm shift, the Proposer-Builder Separation (PBS) scheme. PBS was introduced to decouple the roles of selecting and ordering transactions in a block (i.e., the builder), from those validating its contents and proposing the block to the network as the new head of the blockchain (i.e., the proposer). In this landscape, proposers are the validators in the Proof-of-Stake consensus protocol, while now relying on specialized block builders for creating blocks with the highest value for the proposer. Additionally, relays act as mediators between builders and proposers. We study PBS adoption and show that the current landscape exhibits significant centralization amongst the builders and relays. Further, we explore whether PBS effectively achieves its intended objectives of enabling hobbyist validators to maximize block profitability and preventing censorship. Our findings reveal that although PBS grants validators the opportunity to access optimized and competitive blocks, it tends to stimulate censorship rather than reduce it. Additionally, we demonstrate that relays do not consistently uphold their commitments and may prove unreliable. Specifically, proposers do not always receive the complete promised value, and the censorship or filtering capabilities pledged by relays exhibit significant gaps.