Sana Amjad, Shahid Abbas, Zain Abubaker, Mohammed H. Alsharif · 6 authors
This paper proposes a blockchain-based node authentication model for the Internet of sensor things (IoST). The nodes in the network are authenticated based on their credentials to make the network free from malicious nodes. In IoST, sensor nodes gather the information from the environment and send it to the cluster heads (CHs) for additional processing. CHs aggregate the sensed information. Therefore, their energy rapidly depletes due to extra workload. To solve this issue, we proposed distance, degree, and residual energy-based low-energy adaptive clustering hierarchy (DDR-LEACH) protocol. DDR-LEACH is used to replace CHs with the ordinary nodes based on maximum residual energy, degree, and minimum distance from BS. Furthermore, storing a huge amount of data in the blockchain is very costly. To tackle this issue, an external data storage, named as interplanetary file system (IPFS), is used. Furthermore, for ensuring data security in IPFS, AES 128-bit is used, which performs better than the existing encryption schemes. Moreover, a huge computational cost is required using a proof of work consensus mechanism to validate transactions. To solve this issue, proof of authority (PoA) consensus mechanism is used in the proposed model. The simulation results are carried out, which show the efficiency and effectiveness of the proposed system model. The DDR-LEACH is compared with LEACH and the simulation results show that DDR-LEACH outperforms LEACH in terms of energy consumption, throughput, and improvement in network lifetime with CH selection mechanism. Moreover, transaction cost is computed, which is reduced by PoA during data storage on IPFS and service provisioning. Furthermore, the time is calculated in the comparison of AES 128-bit scheme with existing scheme. The formal security analysis is performed to check the effectiveness of smart contract against attacks. Additionally, two different attacks, MITM and Sybil, are induced in our system to show our system model's resilience against cyber attacks.
Chenchen Han, Gwang-Jun Kim Chenchen Han, Osama Alfarraj Gwang-Jun Kim, Amr Tolba Osama Alfarraj · 5 authors
<p>With the rapid development of 6G communication technology, data security of the Internet of Things (IoT) has become a key challenge. This paper first analyzes the security issues and risks of IoT data storage in 6G, and then constructs a blockchain-based zero-trust data storage scheme (ZT-BDS) in 6G edge IoT to ensure data security. Under this framework, an improved scratch-off puzzle based on Proof of Recoverability (PoR) is firstly constructed to realize distributed IoT data storage, which can reduce resource consumption compared with other existing schemes. Secondly, the accumulator is used to replace the Merkle trees to store IoT data in the blockchain. Since the accumulator can provide not only membership proof, but also non-membership proof, the proposed blockchain-based data storage scheme is more secure. Thirdly, PoW is replaced by an improved PoR scheme as the consensus protocol. On the one hand, PoR can verify the integrity of data, which will further enhance the security of IoT data; on the other hand, the proposed PoR is composed of polynomial commitment, which can reduce bandwidth with the aid of the aggregation function of polynomial commitment. Experimental comparisons show that our scheme has better bandwidth and storage capacity.</p> <p>&nbsp;</p>
The purposes are to develop the patent data profoundly, control the data access process effectively, and protect the patent information and content. The traditional patent review systems are analyzed. For the present patent data security and privacy protection technologies and algorithms, the patent information data are stored on different block nodes after data fragmentation using blockchain technology. Then the data are shared using the data encryption algorism. In this way, data access control can be restricted to particular users. Finally, a patent data protection scheme based on privacy protection is proposed. The security of the scheme and the model performance are verified through simulation experiments. The time required to encrypt 10 MB files with 64-bit and 128-bit data is 35 ms and 105 ms, respectively. The proposed re-encryption algorithm only needs 1 s to decrypt 64 KB data, and only 1% of the data needs asymmetric encryption. This greatly reduces the computational overhead of encryption. Results demonstrate that the system can effectively control the access methods of users, efficiently protect the personal privacy and patent content of patent applicants, and reduce the patent office cloud computing overhead using the local resources of branches. The distributed storage methods can reduce the cloud system interaction of the patent office, thereby greatly improving the speed of encryption and ensuring data security. Compared with the state of the art methods, the proposed patent data access and protection system based on blockchain technology have greater advantages in data security and model performance. The research results can provide a research foundation and practical value for the protection and review systems of patent data.
With the increasing number of coronavirus disease 2019 cases and worldwide vaccination coverage, 'vaccination passports' (vaccination certificates) may become a required permit for global travel, thereby supporting economic recovery. On March 7, 2021, Wang Yi, State Councilor and Foreign Minister of China, announced the launch of the Chinese version of an 'international travel health certificate' at a press conference of the National People's Congress and the Chinese Political Consultative Conference. He proposed a feasible 'Chinese solution' for promoting the recovery of the global economy and the facilitation of cross-border travel, and hoped that the international travel health certificate and vaccination passport can be mutually authenticated. The Israeli government issued a
Open access
Artificial Intelligence in Healthcare and Education
Mohammad Alamgeer, Saud S. Alotaibi, Shaha Al‐Otaibi, Nazik Alturki · 8 authors
Latest developments in computing and communication technologies are enabled the design of connected healthcare system which are mainly based on IoT and Edge technologies. Blockchain, data encryption, and deep learning (DL) models can be utilized to design efficient security solutions for IoT healthcare applications. In this aspect, this article introduces a Blockchain with privacy preserving image encryption and optimal deep learning (BPPIE-ODL) technique for IoT healthcare applications. The proposed BPPIE-ODL technique intends to securely transmit the encrypted medical images captured by IoT devices and performs classification process at the cloud server. The proposed BPPIE-ODL technique encompasses the design of dragonfly algorithm (DFA) with signcryption technique to encrypt the medical images captured by the IoT devices. Besides, blockchain (BC) can be utilized as a distributed data saving approach for generating a ledger, which permits access to the users and prevents third party’s access to encrypted data. In addition, the classification process includes SqueezeNet based feature extraction, softmax classifier (SMC), and Nadam based hyperparameter optimizer. The usage of Nadam model helps to optimally regulate the hyperparameters of the SqueezeNet architecture. For examining the enhanced encryption as well as classification performance of the BPPIE-ODL technique, a comprehensive experimental analysis is carried out. The simulation outcomes demonstrate the significant performance of the BPPIE-ODL technique on the other techniques with increased precision and accuracy of 0.9551 and 0.9813 respectively.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Blockchain technology is widely used in finance, supply chain, Internet of Things and other fields because of its advantages of anti-tampering, decentralization, and traceability. As the core factor affecting the performance of blockchain, consensus algorithm with good performance is the current research focus and goal. Aiming at the problem of insufficient performance and scalability of Practical Byzantine Fault Tolerance (PBFT), a two-stage verification algorithm is proposed. The algorithm improves the three-stage verification of PBFT into the confirmation stage and the review stage. The block contains the confirmation information of the previous block, and the block release and information confirmation are carried out synchronously, which saves the communication cost and reduces the number of communications between nodes, so that the As the system throughput increases, the impact of network scale on performance becomes smaller. The simulation shows that the performance of the algorithm is improved by 50% compared with the PBFT algorithm. After the node reaches the maximum number of connections, the algorithm is limited by the size of the node and becomes smaller, and the scalability of the system is improved.
In recent years, blockchain technology has experienced tremendous growth in application development and gained a lot of attention from people. Blockchain technology-based applications are rapidly expanding in a variety of domains, including government, education, the energy sector, and the internet of things (IoT), among many others. Blockchain is a decentralised and peer-to-peer system that permits the recording of digital transactions in a distributed, encrypted, and secure manner. Even though blockchain technology has the potential to provide more efficient and reliable applications, challenges and obstacles are also present. This paper gives a rigorous overview of the underlying concepts of blockchain: distributed ledger technology, smart contracts, and other features; with the challenges and the corresponding solutions also discussed at the end. This paper also surveyed the blockchain applications in the education ecosystem that are tied to student credentials.
Paras Jain, Sunita Dwivedi, Adel R. Alharbi, R. Sureshbabu · 7 authors
Through the use of blockchain technology, sensitive information may be securely communicated without the need to replicate it, which can assist in decreasing medical record mistakes and saving time by eliminating the need to duplicate information. Furthermore, the information is timestamped, which further enhances the security of the data even further. The deployment of blockchain technology in a range of healthcare situations may enhance the security and efficiency of payment transactions. In this way, only those who have been allowed access to patient medical information can see or modify such information. It is proposed in this study that blockchain technology be used to provide an accessible data storage and retrieval mechanism for patients and healthcare professionals in a healthcare system that is both safe and efficient. As of 1970, a variety of traditional knowledge‐based approaches such as Personal Identification Recognition Number (PIRN), passwords, and other similar methods have been made available; however, many token‐based approaches such as drivers’ licenses, passports, credit cards, bank accounts, ID cards, and keys have also been made available; however, they have all failed to establish a secure and reliable transaction channel. Because they are easily misplaced, stolen, or lost, they are usually unable to protect secrecy or authenticate the identity of a legitimate claimant. Aside from that, personally identifiable information such as passwords and PINs is very prone to fraud since they are easily forgotten or guessed by an imposter. Biometric identification and authentication (commonly known as biometrics) are attracting a great deal of attention these days, particularly in the realm of information security systems, due to its inherent potential and advantages over other conventional ways for identifying and authenticating. As a result of the device’s unique biological characteristics, which include features such as fingerprints, facepalms, hand geometry (including the iris), and the device’s iris, it can be used in a variety of contexts, such as consumer banking kiosks, airport security systems, international ports of entry, universities, office buildings, and forensics, to name a few. It is also used in several other contexts, including forensics and law enforcement. Consequently, every layer of the system—sensed data, computation, and processing of data, as well as the storage and administration of data—is susceptible to a broad variety of threats and weaknesses (cloud). There does not seem to be any suitable methods for dealing with the large volumes of data created by the fog computing architecture when normal data storage and security technologies are used. Because of this, the major objective of this research is to design security countermeasures against medical data mining vulnerabilities that originate from the sensing layer and data storage in the Internet of Things’ cloud database, both of which are discussed in more depth further down. A key allows for the creation of a distributed ledger database and provides an immutable security solution, transaction transparency, and the prohibition of tampering with patient information. This mechanism is particularly useful in healthcare settings, where patient information must be kept confidential. When used in a hospital environment, this method is extremely beneficial. As a result of incorporating blockchain technology into the fog paradigm, it is possible to alleviate some of the current concerns associated with latency, centralization, and scalability.
Samuel D. Hannah, A. J. Deepa, Varghese S. Chooralil, S Sangeetha · 11 authors
Remote health monitoring can help prevent disease at the earlier stages. The Internet of Things (IoT) concepts have recently advanced, enabling omnipresent monitoring. Easily accessible biomarkers for neurodegenerative disorders, namely, Alzheimer's disease (AD) are needed urgently to assist the diagnoses at its early stages. Due to the severe situations, these systems demand high-quality qualities including availability and accuracy. Deep learning algorithms are promising in such health applications when a large amount of data is available. These solutions are ideal for a distributed blockchain-based IoT system. A good Internet connection is critical to the speed of these system responses. Due to their limited processing capabilities, smart gateway devices cannot implement deep learning algorithms. In this paper, we investigate the use of blockchain-based deep neural networks for higher speed and delivery of healthcare data in a healthcare management system. The study exhibits a real-time health monitoring for classification and assesses the response time and accuracy. The deep learning model classifies the brain diseases as benign or malignant. The study takes into account three different classes to predict the brain disease as benign or malignant that includes AD, mild cognitive impairment, and normal cognitive level. The study involves a series of processing where most of the data are utilized for training these classifiers and ensemble model with a metaclassifier classifying the resultant class. The simulation is conducted to test the efficacy of the model over that of the OASIS-3 dataset, which is a longitudinal neuroimaging, cognitive, clinical, and biomarker dataset for normal aging and AD, and it is further trained and tested on the UDS dataset from ADNI. The results show that the proposed method accurately (98%) responds to the query with high speed retrieval of classified results with an increased training accuracy of 0.539 and testing accuracy of 0.559.
With the popularity of IoT devices and cloud technology in the medical industry. Sharing EHRs (Electronic Health Records) among medical institutions improves the accuracy of medical diagnosis and promotes the development of public medical. However, it is difficult to share EHRs among hospitals, and patients typically don’t know about the usage of their health records. In this paper, we propose a patient-controlled EHRs sharing scheme based on cloud computing collaborating blockchain technology. The medical abstract and the access strategy are stored in the blockchain to avoid being tampered with. To achieve the fine-grained access control, we propose the attribute-based encryption scheme and multi-keyword encryption scheme to encrypt EHRs. Moreover, we proposed a node-state-checkable Practical Byzantine Fault Tolerance consensus algorithm (sc-PBFT) to prevent the Byzantine nodes from sneaking into the consortium blockchain. First, we check the state of the elected master node to avoid the master node having any malicious records. Then, using pre-prepared, prepare, and commit processes to complete the consensus request submitted by the client. At last, the proposed consensus algorithm evaluates the state of the master node according to the completion of the three-stage process to reduce the impact of the malicious node on the whole consortium blockchain. By doing this, the malicious node will be marked and isolated into the isolation area. The experimental results show that the proposed sc-PBFT algorithm has better handling capability and lower consensus latency. Compared with the PBFT algorithm in the case of Byzantine nodes, sc-PBFT not only improves the robustness of the consortium blockchain network but also improves the handling capability.
Abdullah Ayub Khan, Asif Ali Laghari, Zaffar Ahmed Shaikh, Zdzisława Dacko-Pikiewicz · 5 authors
With the rapid enhancement in the design and development of the Internet of Things creates a new research interest in the adaptation in industrial domains. It is due to the impact of distributed emerging technology and topology of industrial Internet of Things and the security-related resource constraints of industrial 5.0. This conducts new paradigm along with critical challenges to the existing information preservation, node transactions and communication, transmission, trust and privacy, and security protection related problems. These critical aspects pose serious limitations and issues for the industry to provide industrial data integrity, information exchange reliability, provenance, and trustworthiness for the overall activities and service delivery prospects. In addition, the intersection of blockchain and industrial IoT has gained more consideration and research interest. However, there is an emerging limitation between the inadequate performance of industrial IoT and connected nodes, and the high resource requirement of permissioned private blockchain ledger has not yet been tackled with the complete solution. Due to the introductions of NuCypher Re-Encryption infrastructure, hashing tree and allocation, and deployment of blockchain proof-of-work required more computational power as well. This paper is divided into three different folds; first, we studied various related literature of blockchain-enabling industrial Internet of Things and its critical implementation challenging aspects along with the solution. Secondly, we proposed a blockchain hyperledger sawtooth-enabled framework. This framework provides a secure and trusted execution environment, in which service delivery mechanisms and protocols are designed with an acknowledgment, including the immutable ledger storage security, along with the peer-to-peer network on-chain and off-chain communication of industrial activities. Thirdly, we design pseudo-chain codes and consensus protocols to provide smooth industrial node streamline transactions and broadcast content. The proposed multiple proof-of-work investigated and simulated using Hyperledger Sawtooth-enabled docker for testing to exchange information between connected devices of industrial Internet of Things within the limited usage of resource constraints.
Blockchain technology has the characteristics of decentralization, traceability and tamper proof, which creates a reliable decentralized transaction mode, further accelerating the development of the blockchain platforms. However, with the popularization of various financial applications, security problems caused by blockchain digital assets, such as money laundering, illegal fundraising and phishing fraud, are constantly on the rise. Therefore, financial security has become an important issue in the blockchain ecosystem, and identifying the types of accounts in blockchain (e.g. miners, phishing accounts, Ponzi contracts, etc.) is of great significance in risk assessment and market supervision. In this paper, we construct an account interaction graph using raw blockchain data in a graph perspective, and proposes a joint learning framework for account identity inference on blockchain with graph contrast. We first capture transaction feature and correlation feature from interaction graph, and then perform sampling and data augmentation to generate multiple views for account subgraphs, finally jointly train the subgraph contrast and account classification task. Extensive experiments on Ethereum datasets show that our method achieves significant advantages in account identity inference task in terms of classification performance, scalability and generalization.
In order to ensure security and ledger consistency, in the identity management system, asymmetric cryptography and distributed consensus algorithms have been proposed. These advancements will allow user to have digital ID on his own device, like a smartphone, which he can share with service providers conveniently and securely through a DLT.
Muhammad Shafay, Raja Wasim Ahmad, Khaled Salah, Ibrar Yaqoob · 6 authors
Deep learning has gained huge traction in recent years because of its potential to make informed decisions. A large portion of today’s deep learning systems are based on centralized servers and fall short in providing operational transparency, traceability, reliability, security, and trusted data provenance features. Also, training deep learning models by utilizing centralized data is vulnerable to the single point of failure problem. In this paper, we explore the importance of integrating blockchain technology with deep learning. We review the existing literature focused on the integration of blockchain with deep learning. We classify and categorize the literature by devising a thematic taxonomy based on seven parameters; namely, blockchain type, deep learning models, deep learning specific consensus protocols, application area, services, data types, and deployment goals. We provide insightful discussions on the state-of-the-art blockchain-based deep learning frameworks by highlighting their strengths and weaknesses. Furthermore, we compare the existing blockchain-based deep learning frameworks based on four parameters such as blockchain type, consensus protocol, deep learning method, and dataset. Finally, we present important research challenges which need to be addressed to develop highly efficient, robust, and secure deep learning frameworks.
Abstract In recent times, advanced developments in healthcare sector result in the generation of massive amounts of electronic health records (EHRs). EHR system enables the data owner to control his/her data and share it with designated people. The vast volume of data in the healthcare system makes it difficult for data to ensure security and diagnostic processes. To resolve these issues, this paper develops a new hyperledger blockchain enabled secure medical data management with deep learning (DL)-based diagnosis (HBESDM-DLD) model. The presented model involves distinct stages of operations such as encryption, optimal key generation, hyperledger blockchain-based secure data management, and diagnosis. The presented model allows the user to control access to data, permit the hospital authorities to read/write data, and alert emergency contacts. For encryption, SIMON block cipher technique is applied. At the same time, to improve the efficiency of the SIMON technique, a group teaching optimization algorithm (GTOA) is applied for the optimal key generation of the SIMON technique. Moreover, the sharing of medical data takes place using multi-channel hyperledger blockchain that utilizes a blockchain for storing patient visit data and for the medical institutions to record links for the EHRs saved in external databases. Once the data are decrypted at the receiving end, finally, variational autoencoder (VAE)-based diagnostic model is applied to detect the existence of the diseases. The performance validation of the HBESDM-DLD model takes place on benchmark medical dataset and the results are inspected under various performance measures. The experimental results proves that the HBESDM-DLD methodology is superior to state-of-the-art methods.
Blockchain is a reliable and innovative technology that harnesses education and training through digital technologies. Nonetheless, it has been still an issue keeping track of student/graduate academic achievement and blockchain access rights management. Detailed information about academic performance within a certain period (semester) is not present in the official education documents. Furthermore, academic achievement documents issued by institutions are not secured against unauthorized changes due to the involvement of intermediaries. Therefore, verification of official educational documents has become a pressing issue owing to the recent development of digital technologies. However, effective tools to accelerate the verification are rare as the process takes time. This study provides a prototype of the UniverCert platform based on a consortium version of the decentralized, open-source Ethereum blockchain technology. The proposed platform is based on a globally distributed peer-to-peer network that allows educational institutions to partner with the blockchain network, track student data, verify academic performance, and share documents with other stakeholders. The UniverCert platform was developed on a consortium blockchain architecture to address the problems universities face in storing and securing student data. The system provides a solution to facilitate students’ registration, verification, and authenticity of educational documents.
Cloud applications that work on medical data using blockchain is used by managers and doctors in order to get the image data that is shared between various healthcare institutions. To ensure workability and privacy of the image data, it is important to verify the authenticity of the data, retrieve cypher data and encrypt plain image data. An effective methodology to encrypt the data is the use of a public key authenticated encryption methodology which ensures workability and privacy of the data. But, there are a number of such methodologies available that have been formulated previously. However, the drawback with those methodologies is their inadequacy in protecting the privacy of the data. In order to overcome these disadvantages, we propose a searchable encryption algorithm that can be used for sharing blockchain- based medical image data. This methodology provides traceability, unforgettable and non-tampered image data using blockhain technology, overcoming the drawbacks of blockchain such as computing power and storage. The proposed work will also sustain keyword guessing attacks apart from verification of authenticity and privacy protection of the image data. Taking these factors into consideration, it is determine that there is much work involved in providing stronger security and protecting privacy of data senders. The proposed methodology also meets the requirement of indistinguishability of trapdoor and ciphertext. The highlights of the proposed work are its capability in improving the performance of the system in terms of security and privacy protection.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Chun‐Wei Tsai, Yi‐Ping Phoebe Chen, Tzu‐Chieh Tang, Yuchen Luo
The unlimited possibilities of machine learning have been shown in several successful reports and applications. However, how to make sure that the searched results of a machine learning system are not tampered by anyone and how to prevent the other users in the same network environment from easily getting our private data are two critical research issues when we immerse into powerful machine learning-based systems or applications. This situation is just like other modern information systems that confront security and privacy issues. The development of blockchain provides us an alternative way to address these two issues. That is why some recent studies have attempted to develop machine learning systems with blockchain technologies or to apply machine learning methods to blockchain systems. To show what the combination of blockchain and machine learning is capable of doing, in this paper, we proposed a parallel framework to find out suitable hyperparameters of deep learning in a blockchain environment by using a metaheuristic algorithm. The proposed framework also takes into account the issue of communication cost, by limiting the number of information exchanges between miners and blockchain.
Abstract In order to improve the revenue of attacking mining pools and miners under block withholding attack, we propose the miner revenue optimization algorithm (MROA) based on Pareto artificial bee colony in blockchain network. MROA establishes the revenue optimization model of each attacking mining pool and revenue optimization model of entire attacking mining pools under block withholding attack with the mathematical formulas such as attacking mining pool selection, effective computing power, mining cost and revenue. Then, MROA solves the model by using the modified artificial bee colony algorithm based on the Pareto method. Namely, the employed bee operations include evaluation value calculation, selection probability calculation, crossover operation, mutation operation and Pareto dominance method, and can update each food source. The onlooker bee operations include confirmation probability calculation, crowding degree calculation, neighborhood crossover operation, neighborhood mutation operation and Pareto dominance method, and can find the optimal food source in multidimensional space with smaller distribution density. The scout bee operations delete the local optimal food source that cannot produce new food sources to ensure the diversity of solutions. The simulation results show that no matter how the number of attacking mining pools and the number of miners change, MROA can find a reasonable miner work plan for each attacking mining pool, which increases minimum revenue, average revenue and the evaluation value of optimal solution, and reduces the spacing value and variance of revenue solution set. MROA outperforms the state of the arts such as ABC, NSGA2 and MOPSO.
The Internet of Things (IoT) is considered a high-impact technology in many major markets. It promises many opportunities, but there are several risks and drawbacks associated with this technology stand in the way of realizing its potential opportunities, such as the IoT devices limited capabilities (restricted resources) as well as the current access control systems based on central structures and central hierarchies. with all IoT devices, the security risks and threats are enormous. Blockchain is defined as tamper-proof, tamper-resistant digital ledgers that are executed in a distributed fashion, i.e., without a central repository and usually without central authority, allowing all parties to track information over an insecure network without the need for third-party verification. This technology could be a solution to the problems facing the Internet of Things, however the integrating between blockchain and Internet of Things system comes with many challenges as such blockchain suffers from high resource consumption, slow response, etc. In this research we will propose a new architecture for how to integrate blockchain with the Internet of Things. This structure consists of the smart home responsible for its own data (own chain) and the service provider which considered indirect manager of the network. The performance of the proposed architecture will be studied in terms of expansion, decentralization, security and permanent availability.
The sweeping scale embracing of the smart things has a profound effect in bringing about big data explosion and sophisticated cyber threats at the network edge. Edge computing needs novel security architectures to address the security challenges in severely resource-constrained and heterogeneous edge systems. The successful design of such architectures solves the challenges of scalability [handling massive Internet of Things (IoT) devices], resource constraints and high latency for critical applications. The existing security architectures such as the cloud fail to satisfy these requirements due to remoteness and centralization. This inculcates the need for a distributed architecture that offloads overheads from the cloud and IoT devices by deploying security parameters and operations in proximity. Specifically, the proliferation of distributed architecture integrated with intelligent algorithms such as artificial intelligence (AI) has paramount importance in providing a security collaboration, autonomy and efficiency for the IoT with an improved level of robustness and resilience. However, the state-of-the-art distributed architectures are victims of integrity cyber-attacks such as insider and adversarial attacks. To solve these problems, inspired by the applications in financial industries, the emerging distributed ledger-based technology architecture can significantly boost the robustness of edge network if it is adopted for edge security architecture that can leverage AI algorithms. In this chapter, the architectures of edge computing, specifically, blockchain-based edge architecture which is fueled with the power of AI algorithms, have been proposed and analyzed.
To solve the problem that the safety data in the process of coal mine production are easy to be maliciously tampered with and deleted, a mine consortium blockchain data security monitoring system is proposed. The coal mine consortium blockchain includes supervision department, builds favourable centralized and decentralized production mode, and improves PBFT (Practical Byzantine Fault Tolerance) consensus mechanism to implement practical coal mine safety production. The evaluation shows that the architecture we proposed is more appropriate and efficient for the mine Internet of Things than the traditional blockchain architecture. The Hyperledger Fabric platform is used to build the mine consortium blockchain system to achieve the sensor data reliability, node consensus, safe operation automation management, and major equipment traceability.
Blockchain is an innovative distributed ledger technology that is widely used to build next-generation applications without the support of a trusted third party. With the ceaseless evolution of the service-oriented computing (SOC) paradigm, Blockchain-as-a-Service (BaaS) has emerged, which facilitates development of blockchain-based applications. To develop a high-quality blockchain-based system, users must select highly reliable blockchain services (peers) that offer excellent quality-of-service (QoS). Since the vast number of blockchain services leading to sparse QoS data, selecting the optimal personalized services is challenging. Hence, we improve neural collaborative filtering and propose a QoS-based blockchain service reliability prediction algorithm under BaaS, named modified neural collaborative filtering (MNCF). In this model, we combine a neural network with matrix factorization to perform collaborative filtering for the latent feature vectors of users. Furthermore, multi-task learning for sharing different parameters is introduced to improve the performance of the model. Experiments based on a large-scale real-world dataset validate its superior performance compared to baselines.