Image authentication is an important field that employs many different approaches and has several significant applications. In the proposed approach, we used a combination of two techniques to achieve authentication. Image watermarking is one of the techniques that has been used in many studies but the authentication field still needs to be studied. Blockchain technology is a relatively new technology that has significant research potential related to image authentication. The watermark is embedded into the third-level discrete wavelet transform (DWT) in the middle frequency regions to achieve security and imperceptibility goals. Peak signal-to-noise ratio PSNR, structural similarity matrix (SSIM), normalized correlation coefficient (NCC), and bit error rate (BER) are used to measure the performance of image watermarking. We used blockchain technology to avoid involving a trusted third party for authentication. Secure Hash Algorithm 256 (SHA-256) is applied on the watermark to save it into the blockchain. The watermark is encrypted using Advanced Encryption Standard (AES) and embedded into the image. The proposed method is tested on the USP SICI database and the MedPix medical image database. Ethereum blockchain is used to provide security, anonymity, and integrity of data with no third-party intervention. The proposed solution demonstrates enhanced security for image authentication compared with the state-of-the-art.
Open access
Advanced Steganography and Watermarking Techniques
Blockchain is a decentralised, trust-free distributed ledger technology that has been applied in various fields such as finance, supply chain, and asset management. However, the network isolation between blockchains has limited their interoperability in asset exchange and business collaboration since it forms blockchain islands. Cross-blockchain is an important technology aiming to realise the interoperability between blockchains, and has become one of the hottest research topics in this area. This paper proposes a multi-hop cross-blockchain transaction model based on an improved hash-locking consulted by the notary and users. It can solve the security problems in the traditional hash-locking, and prevent malicious participants from creating a large number of transactions to block cross-blockchain system. Moreover, a notary multi-signature scheme is designed to solve the problem of lack of trust in the traditional model. A multi-hop cross-blockchain transaction loop is designed based on the loop detection method of directed graphs. The transaction process of key agreement, asset locking, lock releasing and security analysis based on the model is discussed in detail. Experiments of cross-blockchain transactions are carried out in Ethereum private chain, and prove that the proposed model has good applicability.
At present, the traditional blockchain for data storage and retrieval reflects the characteristics of slow data uploading speed, high cost, and transparency, and there are a lot of corresponding problems, such as not supporting private data storage, large data operation costs, and not supporting Data field query. This paper proposes a method of data encryption storage and retrieval based on the IOTA distributed ledger, combined with the fast transaction processing speed and zero-value transactions of the IOTA blockchain, through the Masked Authenticated Messaging technology, so that the data is encrypted in the data stream. The form is stored in the distributed ledger, quickly retrieved through the field index mechanism established by the data form, and the data operation is carried out on the chain. Experimental results show that this system has high storage, encryption and retrieval performance, and good practicability.
The sixth-generation (6G) system is a future standard of wireless networks and will be expected to deploy with the full support of blockchain. Blockchain technology is a Distributed Ledger Technology (DLT) to store and refer to the data. In this paper, firstly, we discuss the consensus protocol in 6G and then study the applications of blockchain in 6G. As a result, it has been observed that 27.5% of texts rely on PoW, 22.5% of texts are related to PoS, 17.5% of texts are based on DPOS and PBFT consensus protocol. Blockchain technologies in the state-of-the-art results with 6G, offer various applications such as spectrum management, trusted database, service level agreement, fair access through a smart contract. However, this integration suffers resource utilisation problems, computational loads, delays and bandwidth overhead, leading to a new set of issues that can be solved with the integration of blockchain, IoT and AI with 6G.
Elliptic curve digital signature algorithm (ECDSA) is the simulation of digital signature algorithm (DSA) algorithm on elliptic curve. Compared with DSA, ECDSA has higher security and is the only widely accepted ECDSA, which has been adopted by many standardisation organisations. Based on the study of the original ECDSA scheme, this paper attempts to propose a new improved scheme. The proposed scheme has one main improvement. That is, considering that the original scheme has a finite field inversion process in the signature equation, the time-consuming inversion operation is completely avoided in the design. The proposed scheme has faster computation speed and reduces the ratio of verifying signature to signature generation time. The algorithm has certain significance for improving the efficiency of elliptic curve cryptography. Our simulation results show that the scheme runs faster and has higher signature and verification efficiency than that of the original scheme without compromising security. What's more, we also explore its application in bitcoin and Internet of Things (IoT).
Dayu Jia, Junchang Xin, Zhiqiong Wang, Guoren Wang
COVID-19 virus is raging across the planet. In countries where the epidemic is under control, the main mode of virus transmission is through the transport of imported refrigerated food from epidemic areas. Blockchain is a great way for the government to trace every piece of food. However, the high-performance requirements of the blockchain system for nodes limit its wide application. Several sharding-based blockchain systems have been proposed to solve this limitation. Which blocks should be saved by nodes in the sharding-based blockchain system is a new problem. To solve this problem, the optimized data storage method is proposed in this paper. Five features of block popularity are presented, including the objective feature of a block, the objective feature of the block associated with the node, the historical popularity, the hidden popularity and the storage requirements. Then the ELM classifier is used in the optimized model due to its high performance of training and classification. Finally, the experimental results on synthetic data demonstrate the accuracy and efficiency of the optimized data storage model.
With the popularity of blockchain technology, the financial security issues of blockchain transaction networks have become increasingly serious. Phishing scam detection methods will protect possible victims and build a healthier blockchain ecosystem. Usually, the existing works define phishing scam detection as a node classification task by learning the potential features of users through graph embedding methods such as random walk or graph neural network (GNN). However, these detection methods are suffered from high complexity due to the large scale of the blockchain transaction network, ignoring temporal information of the transaction. Addressing this problem, we defined the transaction pattern graphs for users and transformed the phishing scam detection into a graph classification task. To extract richer information from the input graph, we proposed a multi-channel graph classification model (MCGC) with multiple feature extraction channels for GNN. The transaction pattern graphs and MCGC are more able to detect potential phishing scammers by extracting the transaction pattern features of the target users. Extensive experiments on seven benchmark and Ethereum datasets demonstrate that the proposed MCGC can not only achieve state-of-the-art performance in the graph classification task but also achieve effective phishing scam detection based on the target users' transaction pattern graphs.
The Ethereum blockchain is an open-source, decentralized blockchain with functions triggered by smart contract and has voluminous real-time data for analysis using machine learning and deep learning algorithms. Ether is the cryptocurrency of the Ethereum blockchain. Ethereum virtual machine is used to run Turing complete scripts. The data set concerning a block in the Ethereum blockchain with a block number, timestamp, crypto address of the miner, and the block rewards for the miner are explored for K means clustering for clustering miners with a unique crypto address and their rewards. Linear regression and polynomial regression are used for the prediction of the next block reward to the miner. The Long ShortTerm Memory (LSTM) algorithm is used to exploit the Ether market data set for predicting the next ether price in the market. Every kind of price and volume for every four hours is taken for prediction. The root mean square error of 34.9% is obtained for linear regression, the silhouette score is 71% for K-means clustering of miners with same rewards, with the optimal number of clusters obtained by Gap statistic method.
Open access
Blockchain Technology Applications and Security
Brain Tumor Detection and Classification
Advanced Steganography and Watermarking Techniques
The promise and potential of blockchain to drive social impact is enormous. Blockchain will touch every significant industry which people interact with in dayto-day life. Blockchain will enable solutions that are not previously possible. Health sector recently attracted more initiatives than any other industry. Applications for blockchain in health include digital health records exchange and pharmaceutical supply chain management. In many of these areas, blockchain offers a more secure, decentralized and efficient solution than would otherwise be possible. Blockchain and machine learning (ML) technologies are gaining strong momentum and thrust around the world. Blockchain, a disruptive technology, made its big splash with crypto currencies invention and trading. On the other hand, with predictive and descriptive algorithms, ML is making considerable waves in harnessing existing data to identify patterns and gain insights. Congregating the two technologies can only make them super disruptive! Both have the potential to hasten data exploration and analysis as well as intensify transactions security. Additionally, distributed blockchains can be a significant and proven input for ML, which requires big datasets to make quality predictions. It goes without saying that each technology has its degree of complexity, but both artificial intelligence (AI) and blockchain are in situations where they can benefit from each other and help one another. Both these technologies are able to effect and enact upon data in different ways as their combination makes sense, which can take the exploitation of data to new levels. At the same time, the integration of ML and AI into the blockchain, and vice versa, can enhance blockchain's underlying architecture and boost AI's potential. Additionally, blockchain can also make AI more coherent and understandable for tracing and decision-making using ML techniques. Blockchain and its ledger can record all data and variables that go through a decision made under ML. The present chapter focuses on a few standard ML algorithms that are useful in supporting blockchain technology.
With the development of the world economy and network technology, the security and anonymity of the Bitcoin blockchain are increasingly valued by people. At the same time, because Bitcoin does not have a unified regulatory agency, the Bitcoin blockchain has also brought a series of problems, such as drug transactions and online money laundering. Therefore, node information in the Bitcoin blockchain network needs to be collected and analyzed. Problematic transactions should be analyzed and the source should be traced accurately. This article mainly explains the basic technical principles and data structure of the Bitcoin blockchain, and summarizes the latest research on the data analysis of the Bitcoin blockchain network nodes in recent years. At the same time, relevant data for the last 100,000 blocks in the Bitcoin blockchain are collected and the recent changes in the data of the Bitcoin blockchain network nodes are showed in preparation for further analysis. Finally, the analysis of node data of the Bitcoin blockchain network is summarized and prospected.
Blockchain Technology Applications and Security
Brain Tumor Detection and Classification
Advanced Steganography and Watermarking Techniques
Rajesh Kumar, Wenyong Wang, Jay Kumar, Ting Yang · 7 authors
Deep learning, for image data processing, has been widely used to solve a variety of problems related to medical practices. However, researchers are constantly struggling to introduce ever efficient classification models. Recent studies show that deep learning can perform better and generalize well when trained using a large amount of data. Organizations such as hospitals, testing labs, research centers, etc. can share their data and collaboratively build a better learning model. Every organization wants to retain the privacy of their data, while on the other hand, these organizations want accurate and efficient learning models for various applications. The concern for privacy in medical data limits the sharing of data among multiple organizations due to some ethical and legal issues. To retain privacy and enable data sharing, we present a unique method that combines locally learned deep learning models over the blockchain to improve the prediction of lung cancer in health-care systems by filling the defined gap. There are several challenges involved in sharing that data while maintaining privacy. In this paper, we identify and address such challenges. The contribution of our work is four-fold: (i) We propose a method to secure medical data by only sharing the weights of the trained deep learning model via smart contract. (ii) To deal with different sized computed tomography (CT) images from various sources, we adopted the Bat algorithm and data augmentation to reduce the noise and overfitting for the global learning model. (iii) We distribute the local deep learning model wights to the blockchain decentralized network to train a global model. iv) We propose a recurrent convolutional neural network (RCNN) to estimate the region of interest (ROI) in theCT images. An extensive empirical study has been conducted to verify the significance of our proposed method for better prediction of cancer in the early stage. Experimental results of the proposed model can show that our proposed technique can detect the lung cancer nodules and also achieve better performance.
Robert Alexandru Dobre, Radu Preda, Radu Alexandru Badea, Mihai Stanciu · 5 authors
A notable increase in the distribution of digital images was fueled by the social media platforms. In the fight for attention, the posts containing images have more chances to make users stop scrolling through the crowded news feed than the posts containing only text. The effect is enhanced if the photos that are used in the posts are professional grade. In the context of the COVID-19 pandemic, more and more businesses have moved to online. Every business makes efforts to catch the attention of potential customers with high quality images. These situations could lead to intended or accidental image copyright infringement. This paper proposes a system that can be used to detect and avoid copyright infringement. Photographers can use it to register their photos and businesses can use it to check if the image they want to use is copyright protected or not. If it is, the system also allows the purchase of the right to use the photo. The solution is based on blockchain because of its immutability property. Businesses can look for images on many sites, thus it is very probable that recompressed versions of the same photo would be stored in different places. Therefore, it is important to develop an algorithm that can extract a signature that is resistant to JPEG compression from the image. The signature should be stored on the blockchain along the identification data of the copyright owner.
Advanced Steganography and Watermarking Techniques
Aye Mi San, Nopporn Chotikakamthorn, Chanboon Sathitwiriyawong
Many blockchain-based learning credential systems have been proposed to reduce fraud and improve verification efficiency. In addition to a method for issuing and verifying credentials, a solution is needed to support the revision and revocation of an issued credential record. For the case of learning credentials, depending on how a revocation policy affects credential use that occurred before the revocation date, an additional mechanism may be needed for credential revision. Current digital learning credential methods offer only a revocation mechanism. So they do not fully meet such unique requirement in the education context. In this paper, a blockchain-based method for learning credential revision and revocation is proposed. It makes use of the revision and revocation addresses assigned to each batch of issuing credentials. To revise (revoke) one or more credentials, the proposed method stores the revision (revocation) list as a message in the OP_RETURN field of the revision (revocation) transaction, with the revision (revocation) address as one of its outputs. The concept of a local credential id has been introduced to allow a revision (revocation) list to be efficiently stored on a blockchain system. It is based entirely on a blockchain system and does not require any centralized authority. It is also applicable to most blockchain systems. A comparative study of the proposed method against existing credential revocation methods is also provided.
Abstract The popularity of blockchain, Bitcoin and Ethereum in 2017 can be described as an empty alley. However, the reason why such a popular technology has strong vitality must be to find the most suitable application. In view of the security and privacy, lack of resources, network transmission delay and other issues in the current Internet of Things system, analyze the advantages brought by the introduction of blockchain technology, and compare the architecture of the blockchain Internet of Things with the traditional Internet of Things system. Aiming at the problems existing in the application of blockchain IoT, a blockchain IoT architecture based on edge computing is proposed. In this architecture, the data collected by the edge device is filtered and transmitted to the fog node of the fog layer through the multi-interface base station, and the fog node reports the data processing result to the distributed cloud layer based on the blockchain. The fog layer provides positioning, and the cloud layer provides wide-area monitoring. It provides large-scale event detection, behavior analysis and long-term pattern recognition through distributed computing and storage, and combines blockchain technology to provide scalable, reliable and highly available Internet of Things services. Therefore, the proposed architecture has important reference significance for subsequent computer technology application research based on blockchain IoT technology.
Node identity authentication is an essential means to ensure the security of the Internet of Things. Existing blockchain-based IoT node authentication schemes have many problems. A heterogeneous IoT node authentication scheme based on an improved hybrid blockchain is proposed. Firstly, the hybrid blockchain model is designed to make the blockchain and IoT environment more compatible. Then the proxy node selection mechanism is intended to establish a bridge between the ordinary IoT node and the blockchain, building by calculating the trust value between nodes. Finally, based on the improved hybrid blockchain, the node authentication scheme of the model and proxy node selection mechanism establishes a secure connection for communication between nodes. Safety and performance analysis shows proper safety and performance.
To security support large-scale intelligent applications, distributed machine learning based on blockchain is an intuitive solution scheme. However, the distributed machine learning is difficult to train due to that the corresponding optimization solver algorithms converge slowly, which highly demand on computing and memory resources. To overcome the challenges, we propose a distributed computing framework for L-BFGS optimization algorithm based on variance reduction method, which is a lightweight, few additional cost and parallelized scheme for the model training process. To validate the claims, we have conducted several experiments on multiple classical datasets. Results show that our proposed computing framework can steadily accelerate the training process of solver in either local mode or distributed mode.
Dawid Połap, Gautam Srivastava, Alireza Jolfaei, Reza M. Parizi
In today's technological climate, users require fast automation and digitization of results for large amounts of data at record speeds. Especially in the field of medicine, where each patient is often asked to undergo many different examinations within one diagnosis or treatment. Each examination can help in the diagnosis or prediction of further disease progression. Furthermore, all produced data from these examinations must be stored somewhere and available to various medical practitioners for analysis who may be in geographically diverse locations. The current medical climate leans towards remote patient monitoring and AI-assisted diagnosis. To make this possible, medical data should ideally be secured and made accessible to many medical practitioners, which makes them prone to malicious entities. Medical information has inherent value to malicious entities due to its privacy-sensitive nature in a variety of ways. Furthermore, if access to data is distributively made available to AI algorithms (particularly neural networks) for further analysis/diagnosis, the danger to the data may increase (e.g., model poisoning with fake data introduction). In this paper, we propose a federated learning approach that uses decentralized learning with blockchain-based security and a proposition that accompanies that training intelligent systems using distributed and locally-stored data for the use of all patients. Our work in progress hopes to contribute to the latest trend of the Internet of Medical Things security and privacy.
Mayra Samaniego, Sara Hosseinzadeh Kassani, Cristian Espana, Ralph Deters
Computer-Aided Diagnosis (CAD) systems have emerged to support clinicians in interpreting medical images. CAD systems are traditionally combined with artificial intelligence (AI), computer vision, and data augmentation to evaluate suspicious structures in medical images. This evaluation generates vast amounts of data. Traditional CAD systems belong to a single institution and handle data access management centrally. However, the advent of CAD systems for research among multiple institutions demands distributed access management. This research proposes a blockchain-based solution to enable distributed data access management in CAD systems. This solution has been developed as a distributed application (DApp) using Ethereum in a consortium network.
The Blockchain technology brings a rapid growth in the industry, It emphasizes the service to lead the complexity of software and malicious attack in the network. This technology is used to monitor the highly vulnerable services and it is used to increase the complexity of the warehouse data. It assures the security and consistency of data, The warehouse data has been replicated the availability and the enhancement of security in the services. This technology originated from internet sector as a decentralized, distributed ledger for data transaction. Nowadays, it is visualised as a backbone or frame work for decentralized data processing in open source network. Blockchain uses variety of consensus protocol which is reliable for nodes and communication resources that is used for data consistency. Byzantine fault tolerance algorithm has been proposed for computational cost and security also for consensus efficiency. This paper deals by proposing practical byzantine fault tolerance on edge computing networks paves away for reducing storage overhead also security purpose on edge devices. The proposed model is simulated in the constrain environment and the results are discussed. It shows that the proposed method has increase the availability and security of the stored data.
Blockchain technology has been applied in different fields with the advantages of decentralization, anonymity, immutability and reliability. There are quite a lot of problems, such as data decentralization, low utilization rate, high cost and unguaranteed security when managing students' study records. Therefore, we design a blockchain-based system for students' comprehensive quality assessment. This system adopts a solution of combining “onchain and offchain” data, where we use the smart contract, the interplanetary file system (IPFS) and Web Service to ensure that data flow is efficient, safe and reliable. In this paper, a network architecture based on education consortium blockchain is proposed, where we design a blockchain data structure and storage model to conveniently access to students' quality assessment data. And a data flow mechanism is designed for student data's trusted sharing and authentication protection, in which we combine the advantages of RBFT-based Hyperchain consortium blockchain and traditional data persistence. Then we use the microservices architecture to build the whole system and propose a performance optimization scheme to ensure high availability of the system. Sharing student data for research purposes will boost research innovation in education.
Wireless networks enable wireless-nodes to develop and broadcast messages in an attempt to reinforce congestion protection and performance. Meanwhile, due to distrust environments, it’s mile tough for the wireless-nodes to assess in reliability of the acquired messages. In this work, we advise the decentralized control machine in Wireless networks situated on the blockchain techniques. During this machine, wireless-nodes must be verifying obtained messages from the neighboring Wi-Fi nodes by using Bayesian Inference Model. On the idea of this validation outcome, Wi-Fi node is going to be generated the rating for every message source of wireless-node. With this ranking uploaded from Wi-Fi nodes, Roadside Units are often calculated the trust cost offsets of worried wireless nodes, p.C. This statistic right into the block. Then, to every of the Roadside Unit are going to be attempt for adding their “blocks” to be consider block chain that's maintain with aid of all Roadside Units. Make the utilization of the joint Proof-of-Work and Proof-of-Stake consensus the system, extra overall fee of the offset (stake) is within a block, more easy the Roadside Unit are often located the nonce for their hash feature (evidence-of-paintings).During this manner, all the Roadside Unit collaboratively preserve an up to the date, dependable, and steady believe blockchain. Clone results can display that the proposed gadget be powerful also a possible in accumulating, computing, and storing agrees with values in Wireless networks.