With the SARS-CoV-2's exponential growth, intelligent and constructive practice is required to diagnose the COVID-19. The rapid spread of the virus and the shortage of reliable testing models are considered major issues in detecting COVID-19. This problem remains the peak burden for clinicians. With the advent of artificial intelligence (AI) in image processing, the burden of diagnosing the COVID-19 cases has been reduced to acceptable thresholds. But traditional AI techniques often require centralized data storage and training for the predictive model development which increases the computational complexity. The real-world challenge is to exchange data globally across hospitals while also taking into account of the organizations' privacy concerns. Collaborative model development and privacy protection are critical considerations while training a global deep learning model. To address these challenges, this paper proposes a novel framework based on blockchain and the federated learning model. The federated learning model takes care of reduced complexity, and blockchain helps in distributed data with privacy maintained. More precisely, the proposed federated learning ensembled deep five learning blockchain model (FLED-Block) framework collects the data from the different medical healthcare centers, develops the model with the hybrid capsule learning network, and performs the prediction accurately, while preserving the privacy and shares among authorized persons. Extensive experimentation has been carried out using the lung CT images and compared the performance of the proposed model with the existing VGG-16 and 19, Alexnets, Resnets-50 and 100, Inception V3, Densenets-121, 119, and 150, Mobilenets, SegCaps in terms of accuracy (98.2%), precision (97.3%), recall (96.5%), specificity (33.5%), and F1-score (97%) in predicting the COVID-19 with effectively preserving the privacy of the data among the heterogeneous users.
Open access
COVID-19 diagnosis using AI
Privacy-Preserving Technologies in Data
Artificial Intelligence in Healthcare and Education
In this paper, we present a model of a multi-client framework for access control to datasets put away in an untrusted cloud climate. Distributed storage like some other untrusted climate needs the capacity to get share data. Our methodology gives an entrance command over the information put away in the cloud the supplier investment. The fundamental device of the access control instrument is a ciphertext-strategy trait-based encryption plot with dynamic credits. Utilizing a blockchain-based decentralized record, our framework gives a permanent log of all significant security occasions, for example, key age, access strategy task, change or repudiation, and access demand. We propose a bunch of cryptographic conventions guaranteeing the security of cryptographic tasks requiring mystery or private keys. Just ciphertexts of hash codes are moved through the blockchain record. The model of our framework is executed utilizing shrewd agreements and tried on the Ethereum blockchain stage. Keywords- cloud storage; attribute-based access control; ciphertext-policy attribute-based encryption; blockchain
Location-Based Services (LBSs) are essential in many application contexts like ride-sharing or navigation apps. There are cases where users could gain an advantage by submitting fake locations. The problem faced in this paper concerns the possibility that the geographic location declared by a user is not the actual location in which the user is placed. Some solutions are based on centralized or distributed verification in the literature, and other solutions are based on witnesses or infrastructure. In this paper, we highlight the limitations of such approaches and propose a new scheme that exploits signals coming from satellites to provide trustworthy location proofs, also respecting users' privacy. The proposed approach is decentralized because location proofs are stored by users in a suitably-encrypted way, and a blockchain is adopted to guarantee data integrity and authenticity. We show that the proposed approach overcomes the state of the art through a detailed analysis.
Jiawen Kang, Dongdong Ye, Jiangtian Nie, Jiang Xiao · 9 authors
The emerging industrial metaverses realize the map-ping and expanding operations of physical industry into virtual space for significantly upgrading intelligent manufacturing. The industrial metaverses obtain data from various production and operation lines by Industrial Internet of Things (IIoT), and thus conduct effective data analysis and decision-making, thereby en-hancing the production efficiency of the physical space, reducing operating costs, and maximizing commercial value. However, there still exist bottlenecks when integrating metaverses into IIoT, such as the privacy leakage of sensitive data with commercial secrets, IIoT sensing data freshness, and incentives for sharing these data. In this paper, we design a user-defined privacy-preserving framework with decentralized federated learning for the industrial metaverses. To further improve privacy protection of industrial metaverse, a cross-chain empowered federated learning framework is further utilized to perform decentralized, secure, and privacy-preserving data training on both physical and virtual spaces through a hierarchical blockchain architecture with a main chain and multiple subchains. Moreover, we introduce the age of information as the data freshness metric and thus design an age-based contract model to motivate data sensing among IIoT nodes. Numerical results indicate the efficiency of the proposed framework and incentive mechanism in the industrial metaverses.
Gonzalo Munilla Garrido, Johannes Sedlmeir, Matthias Babel
Analyses that fulfill differential privacy provide plausible deniability to individuals while allowing analysts to extract insights from data. However, beyond an often acceptable accuracy tradeoff, these statistical disclosure techniques generally inhibit the verifiability of the provided information, as one cannot check the correctness of the participants' truthful information, the differentially private mechanism, or the unbiased random number generation. While related work has already discussed this opportunity, an efficient implementation with a precise bound on errors and corresponding proofs of the differential privacy property is so far missing. In this paper, we follow an approach based on zero-knowledge proofs~(ZKPs), in specific succinct non-interactive arguments of knowledge, as a verifiable computation technique to prove the correctness of a differentially private query output. In particular, we ensure the guarantees of differential privacy hold despite the limitations of ZKPs that operate on finite fields and have limited branching capabilities. We demonstrate that our approach has practical performance and discuss how practitioners could employ our primitives to verifiably query individuals' age from their digitally signed ID card in a differentially private manner.
Eman Ashraf, Nihal F. F. Areed, Hanaa Salem, Ehab H. Abdelhay · 5 authors
Recently, there has been considerable growth in the internet of things (IoT)-based healthcare applications; however, they suffer from a lack of intrusion detection systems (IDS). Leveraging recent technologies, such as machine learning (ML), edge computing, and blockchain, can provide suitable and strong security solutions for preserving the privacy of medical data. In this paper, FIDChain IDS is proposed using lightweight artificial neural networks (ANN) in a federated learning (FL) way to ensure healthcare data privacy preservation with the advances of blockchain technology that provides a distributed ledger for aggregating the local weights and then broadcasting the updated global weights after averaging, which prevents poisoning attacks and provides full transparency and immutability over the distributed system with negligible overhead. Applying the detection model at the edge protects the cloud if an attack happens, as it blocks the data from its gateway with smaller detection time and lesser computing and processing capacity as FL deals with smaller sets of data. The ANN and eXtreme Gradient Boosting (XGBoost) models were evaluated using the BoT-IoT dataset. The results show that ANN models have higher accuracy and better performance with the heterogeneity of data in IoT devices, such as intensive care unit (ICU) in healthcare systems. Testing the FIDChain with different datasets (CSE-CIC-IDS2018, Bot Net IoT, and KDD Cup 99) reveals that the BoT-IoT dataset has the most stable and accurate results for testing IoT applications, such as those used in healthcare systems.
Pengxiang Zhao, Jesus Rodrigo Cedeno Jimenez, Maria Antonia Brovelli, Ali Mansourian
Abstract. In recent years, geospatial big data has been generated at a very high speed, and the data volume is becoming increasingly massive. In order to realize the full potential of geospatial big data, there has been a strong requirement and push to embrace the value of open science. However, it is still challenging to preserve the privacy and integrity of geospatial data in data sharing and management. Blockchain as d distributed ledger technology has a series of good characteristics, such as decentralization, trust-free, transparency, tamper-free, consensus and security, etc. These characteristics of blockchain are beneficial for facilitating geospatial data sharing and management, and hence promoting the development of open GIS. In this paper, we provide a comprehensive review on the literature that involves how blockchain technology is applied to geospatial data, especially in geospatial data privacy and integrity preservation. First, the background knowledge on geospatial data privacy and blockchain technology are introduced. Then, we reviewed how blockchain technology is applied to geospatial data, followed by the conclusion of the topic. This review is beneficial for understanding how blockchain technology can be applied to geospatial domain by integrating geospatial technologies like GIS and remote sensing.
Abdul Rehman Javed, Muhammad Abul Hassan, Faisal Shahzad, Waqas Ahmed · 7 authors
The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends mainly on the IoT. Advanced machine learning (ML) techniques are being used to strengthen the STI smartness further. However, some decisions are very challenging due to the vast number of STI components and big data generated from STIs. Computation cost, communication overheads, and privacy issues are significant concerns for wide-scale ML adoption within STI. These issues can be addressed using Federated Learning (FL) and blockchain. FL can be used to address the issues of privacy preservation and handling big data generated in STI management and control. Blockchain is a distributed ledger that can store data while providing trust and integrity assurance. Blockchain can be a solution to data integrity and can add more security to the STI. This survey initially explores the vehicular network and STI in detail and sheds light on the blockchain and FL with real-world implementations. Then, FL and blockchain applications in the Vehicular Ad Hoc Network (VANET) environment from security and privacy perspectives are discussed in detail. In the end, the paper focuses on the current research challenges and future research directions related to integrating FL and blockchain for vehicular networks.
Rafael Belchior, Limaris Torres, Jonas Pfannschmid, André Vasconcelos · 5 authors
Distributed ledger technology (DLT) provides decentralized and tamper-resistant data storage, replicated among mutually untrusting participants. With the advancement of this technology, different privacy-preserving blockchains have been proposed, such as Corda, Hyperledger Fabric, and Digital Asset’s Canton. These distributed ledgers only provide \emph{partial consistency}, which implies that participants can view the same ledger differently. A \emph{view} represents the states of a blockchain available to a particular stakeholder. The combination of views forms an integrated view that represents a consistent global state shared by all participants. This paper introduces BUNGEE (Blockchain UNifier view GEnErator), the first DLT view generator, to allow capturing DLT snapshots, constructing views, and performing arbitrary operations on those, such as integrating views. Creating and integrating views allows interesting applications, such as stakeholder-centric snapshots for audits, cross-chain analysis, blockchain migration, and data analytics.
Rafael Belchior, Limaris Torres, Jonas Pfannschmid, André Vasconcelos · 5 authors
Distributed ledger technology (DLT) provides decentralized and tamper-resistant data storage, replicated among mutually untrusting participants. With the advancement of this technology, different privacy-preserving blockchains have been proposed, such as Corda, Hyperledger Fabric, and Digital Asset's Canton. These distributed ledgers only provide \emph{partial consistency}, which implies that participants can view the same ledger differently. A \emph{view} represents the states of a blockchain available to a particular stakeholder. The combination of views forms an integrated view that represents a consistent global state shared by all participants. This paper introduces BUNGEE (Blockchain UNifier view GEnErator), the first DLT view generator, to allow capturing DLT snapshots, constructing views, and performing arbitrary operations on those, such as integrating views. Creating and integrating views allows interesting applications, such as stakeholder-centric snapshots for audits, cross-chain analysis, blockchain migration, and data analytics.
With the development of mobile edge computing (MEC) and blockchain-based federated learning (BCFL), a number of studies suggest deploying BCFL on edge servers. In this case, resource-limited edge servers need to serve both mobile devices for their offloading tasks and the BCFL system for model training and blockchain consensus in a cost-efficient manner without sacrificing the service quality to any side. To address this challenge, this paper proposes a resource allocation scheme for edge servers, aiming to provide the optimal services with the minimum cost. Specifically, we first analyze the energy consumed by the MEC and BCFL tasks, and then use the completion time of each task as the service quality constraint. Then, we model the resource allocation challenge into a multivariate, multi-constraint, and convex optimization problem. To solve the problem in a progressive manner, we design two algorithms based on the alternating direction method of multipliers (ADMM) in both the homogeneous and heterogeneous situations with equal and on-demand resource distribution strategies, respectively. The validity of our proposed algorithms is proved via rigorous theoretical analysis. Through extensive experiments, the convergence and efficiency of our proposed resource allocation schemes are evaluated. To the best of our knowledge, this is the first work to investigate the resource allocation dilemma of edge servers for BCFL in MEC.
Niki Hrovatin, Aleksandar Tošić, Michaël Mrissa, Branko Kavšek
Currently, the computational power present in the sensors forming a wireless sensor network (WSN) allows for implementing most of the data processing and analysis directly on the sensors in a decentralized way. This shift in paradigm introduces a shift in the privacy and security problems that need to be addressed. While a decentralized implementation avoids the single point of failure problem that typically applies to centralized approaches, it is subject to other threats, such as external monitoring, and new challenges, such as the complexity of providing decentralized implementations for data mining algorithms. In this paper, we present a solution for privacy-aware distributed data mining on wireless sensor networks. Our solution uses a permissioned blockchain to avoid a single point of failure in the system. Contracts are used to construct an onion-like structure encompassing the Hoeffding trees and a route. The onion-routed query conceals the network identity of the sensors from external adversaries, and obfuscates the actual computation to hide it from internally compromised nodes. We validate our solution on a use case related to an air quality-monitoring sensor network. We compare the quality of our model against traditional models to support the feasibility and viability of the solution.
Hien Thi Thu Truong, José L. Hernández-Ramos, Juan A. Martínez, Jorge Bernal Bernabé · 7 authors
The increase in the interconnection of physical devices and the emergence of the 5 G paradigm foster the generation and distribution of massive amounts of data. The complexity associated with the management of these data requires a suitable access control approach that empowers citizens to control how their data are shared, so potential privacy issues can be mitigated. While well-known access control models are widely used in web and cloud scenarios, the IoT ecosystem needs to address the requirements of lightness, decentralization, and scalability to control the access to data generated by a huge number of heterogeneous devices. This work proposes CapBlock, a design that integrates a capability-based access control model and blockchain technology for a fully distributed evaluation of authorization policies and generation of access credentials using smart contracts. CapBlock is intended to manage the access to information in federated IoT environments where data need to be managed through access control policies defined by different data providers. The feasibility of CapBlock has been successfully evaluated in the scope of the EU research project IoTCrawler, which aims at building a secure search engine for IoT data in large-scale scenarios.
Mishri Saleh Al-Marshoud, Ali H. Al‐Bayatti, Mehmet Sabır Kiraz
Security and privacy in vehicular ad hoc networks (VANETs) are challenging in terms of Intelligent Transportation Systems (ITS) features. The distribution and decentralisation of vehicles could threaten location privacy and confidentiality in the absence of trusted third parties (TTP)s or if they are otherwise compromised. If the same digital signatures (or the same certificates) are used for different communications, then adversaries could easily apply linking attacks. Unfortunately, most of the existing schemes for VANETs in the literature do not satisfy the required levels of security, location privacy, and efficiency simultaneously. This paper presents a new and efficient end-to-end anonymous key exchange protocol based on Yang et al. 's self-blindable signatures. In our protocol, vehicles first privately blind their own private certificates for each communication outside the mix-zone and then compute an anonymous shared key based on zero-knowledge proof of knowledge (PoK). The efficiency comes from the fact that once the signatures are verified, the ephemeral values in PoK are also used to compute a shared key through an authenticated Diffie-Hellman key exchange protocol. Therefore, the protocol does not require any further external information to generate a shared key. Our protocol also does not require an interference with the Roadside Units or Certificate Authorities, and hence can be securely run outside the mixed-zones. We demonstrate the security of our protocol in an ideal/real simulation paradigm. Hence, our protocol achieves secure authentication, forward unlinkability, and accountability. Furthermore, the performance analysis shows that our protocol is more efficient in terms of computational and communication overheads compared to existing schemes.
Lejun Zhang, Yanfei Zou, Muhammad Hassam Yousuf, Weizheng Wang · 7 authors
Due to the increasing need for data sharing in the age of big data, how to achieve data access control and implement user permission revocation in the blockchain environment becomes an urgent problem. To solve the above problems, we propose a novel blockchain-based data sharing scheme (BDSS) with fine-grained access control and permission revocation in this paper, which regards the medical environment as the application scenario. In this scheme, we separate the public part and private part of the electronic medical record (EMR). Then, we use symmetric searchable encryption (SSE) technology to encrypt these two parts separately, and use attribute-based encryption (ABE) technology to encrypt symmetric keys which used in SSE technology separately. This guarantees better fine-grained access control and makes patients to share data at ease. In addition, we design a mechanism for EMR permission grant and revocation so that hospital can verify attribute set to determine whether to grant and revoke access permission through blockchain, so it is no longer necessary for ciphertext re-encryption and key update. Finally, security analysis, security proof and performance evaluation demonstrate that the proposed scheme is safe and effective in practical applications.
The application of artificial intelligence technology in Internet of Vehicles (IoV) has attracted great research interests with the goal of enabling smart transportation and traffic management. Meanwhile, concerns have been raised over the security and privacy of the tons of traffic and vehicle data. In this regard, Federated Learning (FL) with privacy protection features is considered a highly promising solution. However, in the FL process, the server side may take advantage of its dominant role in model aggregation to steal sensitive information of users, while the client side may also upload malicious data to compromise the training of the global model. Most existing privacy-preserving FL schemes in IoV fail to deal with threats from both of these two sides at the same time. In this paper, we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL, which uses blockchain as the underlying distributed framework of FL. We improve the Multi-Krum technology and combine it with the homomorphic encryption to achieve ciphertext-level model aggregation and model filtering, which can enable the verifiability of the local models while achieving privacy-preservation. Additionally, we develop a reputation-based incentive mechanism to encourage users in IoV to actively participate in the federated learning and to practice honesty. The security analysis and performance evaluations are conducted to show that the proposed scheme can meet the security requirements and improve the performance of the FL model.
Industrial applications often require federated cloud services from multiple providers to improve reliability and flexibility. Traditional selection methods through auctions usually involve a centralized auctioneer to coordinate the auction procedure. Blockchain and smart contracts provide a decentralized mechanism to automate the cloud auction process; however, existing solutions fail in the selection of the most suitable providers and the violation detection of the signed auction agreements, which are also known as service-level agreements (SLAs). To tackle these problems, we propose an integrated auction model using Bayesian game theory and blockchain techniques. The proposed model is enhanced with two Bayesian Nash Equilibriums (BNEs); the first BNE enables the selection of cost-effective providers to construct the federated cloud services, while the second BNE ensures consistent and trustworthy monitoring of federated SLAs. Moreover, a timed message submission (TMS) algorithm is proposed to protect the auction privacy during the message submission phase. This paper validates the equilibrium results of two BNEs and implements the proposed model on the Ethereum blockchain. The analytical and experimental results demonstrate the feasibility, trustworthiness, and cost-effectiveness of our model.
Minal Patel, Bhavesh N. Gohil, Sanjay Chaudhary, Sanjay Garg
Blockchain enables smart contract for secure data transfer by which fog offloading servers can have trustworthy access control to work with data execution. When cloud is used for handling requests from mobile users, the attacker may perform denial of service attack and the same is possible at fog nodes and the same can be handled with the help of blockchain technology. In this paper, smart city application is discussed a use case study for blockchain based fog computing architecture. We propose a novel offload chain architecture for blockchain-based offloading in internet of things (IoT) networks where mobile devices can offload their data to fog servers for computation by an access control mechanism. The offload chain model using deep reinforcement learning (DRL) is proposed to improve the efficiency of blockchain based fog offloading amongst existing models.
In the modern digital world, large-scale data and the analytic processing of the data make privacy-preserving computation even more critical. SMPC is a cryptographic protocol used to compute a function over the inputs of multiple parties such that the other party’s input is unknown. This then provides for computing in parallel with other participants, without requiring a coordinator, which, in today’s privacy-conscious world, is beneficial in avoiding using a central authority in data-entrusted activities. In a nutshell, a decentralized AI approach is based on distributed computing principles and the blockchain to create a solid architecture for SMPC implementation. In this manner, decentralized AI eliminates several drawbacks of data centralization, such as single points of failure and data breaches. SMPC and decentralized networks are the foundation of the privacy-preserving ML, where sensitive data train models without revealing the data points. Specifically, the growing necessity for protecting data with the help of laws like the GDPR and CCPA enhances SMPC’s application in decentralized AI. Blockchain technology extends this implementation by having additional qualities of having an unchangeable record and consensus mechanisms that guarantee computation reliability and openness. However, scalability, ITY, computational cost, and system compatibility are drawbacks to integrating decentralized AI and SMPC. Solving these needs more be a continuous effort in the search for cryptographic techniques in communication, network design, and protocol formation. The combination of decentralized AI and SMPC presents a new and revolutionary way of multi-party computation through data privacy and access to cooperation and innovation in sectors such as health, finance, and supply chain. With the development of technology, these intelligent computing applications of decentralized AI and SMPC will continue to develop and open up new areas for efficient and secure data usage.
Roseline Oluwaseun Ogundokun, Sanjay Misra, Rytis Maskeliūnas, Robertas Damaševičius
Federated learning (FL) is a scheme in which several consumers work collectively to unravel machine learning (ML) problems, with a dominant collector synchronizing the procedure. This decision correspondingly enables the training data to be distributed, guaranteeing that the individual device’s data are secluded. The paper systematically reviewed the available literature using the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guiding principle. The study presents a systematic review of appliable ML approaches for FL, reviews the categorization of FL, discusses the FL application areas, presents the relationship between FL and Blockchain Technology (BT), and discusses some existing literature that has used FL and ML approaches. The study also examined applicable machine learning models for federated learning. The inclusion measures were (i) published between 2017 and 2021, (ii) written in English, (iii) published in a peer-reviewed scientific journal, and (iv) Preprint published papers. Unpublished studies, thesis and dissertation studies, (ii) conference papers, (iii) not in English, and (iv) did not use artificial intelligence models and blockchain technology were all removed from the review. In total, 84 eligible papers were finally examined in this study. Finally, in recent years, the amount of research on ML using FL has increased. Accuracy equivalent to standard feature-based techniques has been attained, and ensembles of many algorithms may yield even better results. We discovered that the best results were obtained from the hybrid design of an ML ensemble employing expert features. However, some additional difficulties and issues need to be overcome, such as efficiency, complexity, and smaller datasets. In addition, novel FL applications should be investigated from the standpoint of the datasets and methodologies.
Zhen Qin, Xueqiang Yan, MengChu Zhou, Shuiguang Deng
Federated learning (FL) enables collaborative training of machine learning models without sharing training data. Traditional FL heavily relies on a trusted centralized server. Although decentralized FL eliminates the central dependence, it may worsen the other inherit problems faced by FL such as poisoning attacks and data representation leakage due to insufficient restrictions on the behavior of participants, and heavy communication cost, especially in fully decentralized scenarios, i.e., peer-to-peer (P2P) settings. In this paper, we propose a blockchain-based fully decentralized P2P framework for FL, called BlockDFL. It takes blockchain as the foundation, leveraging the proposed PBFT-based voting mechanism and two-layer scoring mechanism to coordinate FL among peer participants without mutual trust, while effectively defending against poisoning attacks. Gradient compression is introduced to lowering communication cost and prevent data from being reconstructed from transmitted model updates. Extensive experiments conducted on two real-world datasets exhibit that BlockDFL obtains competitive accuracy compared to centralized FL and can defend poisoning attacks while achieving efficiency and scalability. Especially when the proportion of malicious participants is as high as 40%, BlockDFL can still preserve the accuracy of FL, outperforming existing fully decentralized P2P FL frameworks based on blockchain.
Widespread applications of 5G technology have prompted the outsourcing of computation dominated by the Internet of Things (IoT) cloud to improve transmission efficiency, which has created a novel paradigm for improving the speed of common connected objects in IoT. However, although it makes it easier for ubiquitous resource-constrained equipment that outsources computing tasks to achieve high-speed transmission services, security concerns, such as a lack of reliability and collusion attacks, still exist in the outsourcing computation. In this paper, we propose a reliable, anti-collusion outsourcing computation and verification protocol, which uses distributed storage solutions in response to the issue of centralized storage, leverages homomorphic encryption to deal with outsourcing computation and ensures data privacy. Moreover, we embed outsourcing computation results and a novel polynomial factorization algorithm into the smart contract of Ethereum, which not only enables the verification of the outsourcing result without a trusted third party but also resists collusion attacks. The results of the theoretical analysis and experimental performance evaluation demonstrate that the proposed protocol is secure, reliable, and more effective compared with state-of-the-art approaches.
Fatemeh Ghovanlooy Ghajar, Axel Sikora, Dominik Welte
Industrial companies can use blockchain to assist them in resolving their trust and security issues. In this research, we provide a fully distributed blockchain-based architecture for industrial IoT, relying on trust management and reputation to enhance nodes’ trustworthiness. The purpose of this contribution is to introduce our system architecture to show how to secure network access for users with dynamic authorization management. All decisions in the system are made by trustful nodes’ consensus and are fully distributed. The remarkable feature of this system architecture is that the influence of the nodes’ power is lowered depending on their Proof of Work (PoW) and Proof of Stake (PoS), and the nodes’ significance and authority is determined by their behavior in the network. This impact is based on game theory and an incentive mechanism for reputation between nodes. This system design can be used on legacy machines, which means that security and distributed systems can be put in place at a low cost on industrial systems. While there are no numerical results yet, this work, based on the open questions regarding the majority problem and the proposed solutions based on a game-theoretic mechanism and a trust management system, points to what and how industrial IoT and existing blockchain frameworks that are focusing only on the power of PoW and PoS can be secured more effectively.
Privacy and data security have become the new hot topic for regulators in recent years. As a result, Federated Learning (FL) (also called collaborative learning) has emerged as a new training paradigm that allows multiple, geographically distributed nodes to learn a Deep Learning (DL) model together without sharing their data. Blockchain is becoming a new trend as data protection and privacy are concerns in many sectors. Technology is leading the world and transforming into a global village where everything is accessible and transparent. We have presented a blockchain enabled security model using FL that can generate an enhanced DL model without sharing data and improve privacy through higher security and access rights to data. However, existing FL approaches also have unique security vulnerabilities that malicious actors can exploit and compromise the trained model. The FL method is compared to the other known approaches. Users are more likely to choose the latter option, i.e., providing local but private data to the server and using ML apps, performing ML operations on the devices without benefiting from other users’ data, and preventing direct access to raw data and local training of ML models. FL protects data privacy and reduces data transfer overhead by storing raw data on devices and combining locally computed model updates. We have investigated the feasibility of data and model poisoning attacks under a blockchain-enabled FL system built alongside the Ethereum network and the traditional FL system (without blockchain). This work fills a knowledge gap by proposing a transparent incentive mechanism that can encourage good behavior among participating decentralized nodes and avoid common problems and provides knowledge for the FL security literature by investigating current FL systems.