Blockchain Papers

Asadi Srinivasulu, K. Ramanjaneyulu, R. Neelaveni, Santoshachandra Rao Karanam · 7 authors

No abstract is available for this record.

Sanath Javagal

The increasing prevalence and sophistication of autonomous vehicles are intricately tied with the advancements in Artificial Intelligence (AI), particularly in refining their network architectures. This technical exposition unfolds the paramount role of AI in autonomous vehicles, spotlighting its capabilities in sensor data processing, decision - making, and fostering Vehicle - to - Everything (V2X) communications. Moreover, the article elucidates the evolution and enhancement of network architectures through AI - driven technologies such as edge computing, centralized data centers, Distributed Ledger Technologies (DLT), and 5G communications. An additional layer of exploration is provided in network security, with AI safeguarding vehicular networks through anomaly detection and ensuring secure data transmission. This interplay between AI and network architecture elevates autonomous vehicles' operational efficiency and safety and acts as a linchpin in realizing a coherent and intelligent transportation ecosystem. The comprehensive integration of AI within the network forms the foundation for autonomous vehicles to navigate within an interconnected mobility infrastructure harmoniously and securely.

Yixiao Lan, Yuan Liu, Boyang Li, Chunyan Miao

The consensus algorithm is the core component of a blockchain system, which determines the efficiency, security, and scalability of the blockchain network. The representative consensus algorithm is the proof of work (PoW) proposed in Bitcoin, where the consensus process consumes large amount of compute in solving meaningless Hash puzzel. Meanwhile, the deep learning (DL) has brought unprecedented performance gains at heavy computate cost. In this demo, we channels the otherwise wasted computational power to the practical purpose of training neural network models, through the proposed proof of learning (PoL) consensus algorithm. In PoLe, the training/testing data are released to the entire blockchain network (BCN) and the consensus nodes train NN models on the data, which serves as the proof of learning. When the consensus on the BCN considers a NN model to be valid, a new block is appended to the blockchain. Through our system, we investigate the potential of enpowering machine learning with consensus building on blockchains.

Sun Ya-hong

Abstract Blockchain originated in 2009, it was the underlying “ledger” recording technology of Bitcoin. After several years of development and improvement, it has gradually become a distributed, decentralized, and trustless technical solution. In the past two years, blockchain has become a new decentralized distributed computing paradigm. We apply blockchain technology into cloud computing, using the former’s security mechanism to improve the latter’s secure storage and secure computing performance. This is a promising research topic. The paper mainly discusses the design and implementation of blockchain from a technical prospect, it summarizes the advantages and disadvantages of the blockchain, and it also analyzes the possible application scenarios.

Sakthi Kumaresh

Blockchain and Artificial intelligence are novel technologies which take prominent place across all industries. AI refers to intelligent tasks that are carried out by machines which in-turn are done by humans in olden days. Blockchain a collection of decentralised networks which revolutionizes and upgrades the business operations. Blockchain creates a decentralised environment that shares data which is in encrypted form between ledgers in a confidential way without the involvement of any third party. Blockchain and Artificial Intelligence has been shaping their paths with a slight overlap of their own. Combined decentralised AI networks enables the businesses to take decisions without the need of centralised control activity. Block chain consists of nodes and it follows distributed ledger technology; Proof of Work (PoW) consensus algorithm used in blockchain makes use of lot of computing power and energy for the miners to get reward. To overcome this difficulty, this paper proposes AI enabled Miner Node Selection Algorithm in Block chain Networks based on PoW consensus algorithm. The paper presents a miner node selection with PoW in blockchain which reduce data storage in blockchain by data pruning technique. By executing this algorithm in blockchain network, unbiased blockchain implementation will be easier. The integration of AI with the implementation of blockchain enhances the efficiency of blockchain network by reducing the computational power, energy and time spent in selection of the node which is evident in our experimental results.

Shakila Zaman, Muhammad R. A. Khandaker, Risala Tasin Khan, Faisal Tariq · 5 authors

The Internet-of-Things (IoT) is an emerging technology that connects and integrates a massive number of smart physical devices with virtual objects operating in diverse platforms through the internet. Due to massive size and physical spread of many applications such as smart healthcare, IoT is increasingly implemented in distributed setting. This distributed nature of implementation of the entities connected to the IoT networks are exposed to an unprecedented level of privacy and security threats. This is particularly severe for IoT healthcare system as it involves huge volume of sensitive and personal data. Although blockchain has posed to be the solution in this scenario thanks to its inherent distributed ledger technology (DLT), it suffers from a major drawback of rapidly increasing storage and computation requirements with the increase in network size which makes its implementation impractical. This paper proposes a holochain-based security and privacy-preserving framework for IoT healthcare systems that overcomes the scalability challenge and is particularly suited for resource constrained IoT scenarios. Through thorough analysis and performance results, we have demonstrated that the holochain based IoT healthcare solution outperforms blockchain based solution in terms of resource requirements while ensuring appropriate level of privacy and security.

Neetha S.S, Michel Rwibasira, R Suchithra

Technology grows up, day to day to facilitate human beings’ lifestyle, in order to protect them and their properties. Cloud computing is a platform that one hardware is shared by different clients in the virtual ways and works as standalone physical hardware, is accessed everywhere at any time through the internet. Security is an essential and a vital point to all customers is belonged in the same physical component. This survey paper discusses about cloud computing challenges, types of attacks, and currently solutions in the details. Furthermore, cloud security of models as application, network, and deployment and also services. Cloud security based on a distributed ledger helps the clients to operate within transparency manners and is decentralized to enhance cloud computing information security principals, cloud security requirements, cloud security control, and security design of cloud computing

Rachit Agarwal, Tanmay Thapliyal, Sandeep K. Shukla

Different types of malicious activities have been flagged in multiple\npermissionless blockchains such as bitcoin, Ethereum etc. While some malicious\nactivities exploit vulnerabilities in the infrastructure of the blockchain,\nsome target its users through social engineering techniques. To address these\nproblems, we aim at automatically flagging blockchain accounts that originate\nsuch malicious exploitation of accounts of other participants. To that end, we\nidentify a robust supervised machine learning (ML) algorithm that is resistant\nto any bias induced by an over representation of certain malicious activity in\nthe available dataset, as well as is robust against adversarial attacks. We\nfind that most of the malicious activities reported thus far, for example, in\nEthereum blockchain ecosystem, behaves statistically similar. Further, the\npreviously used ML algorithms for identifying malicious accounts show bias\ntowards a particular malicious activity which is over-represented. In the\nsequel, we identify that Neural Networks (NN) holds up the best in the face of\nsuch bias inducing dataset at the same time being robust against certain\nadversarial attacks.\n

Tanweer Alam

No abstract is available for this record.

Monika Parmar, Harsimran Jit

Cryptography algorithms play a vital role in Information Security and Management. To test the credibility, reliability of metadata exchanged between the sender and the recipient party of IoT applications different algorithms must be used. The hashing is also used for Electronic Signatures and based on how hard it is to hack them; various algorithms have different safety protocols. SHA-1, SHA-2, SHA3, MD4, and MD5, etc. are still the most accepted hash protocols. This article suggests the relevance of hash functions and the comparative study of different cryptographic techniques using blockchain technology. Cloud storage is amongst the most daunting issues, guaranteeing the confidentiality of encrypted data on virtual computers. Several protection challenges exist in the cloud, including encryption, integrity, and secrecy. Different encryption strategies are seeking to solve these problems of data protection to an immense degree. This article will focus on the comparative analysis of the SHA family and MD5 based on the speed of operation, its security concerns, and the need of using the Secure Hash Algorithm.

Abdullah Lakhan, Mazhar Ali Dootio, Ali Hassan Sodhro, Sandeep Pirbhulal · 7 authors

These days, healthcare applications on the Internet of Medical Things (IoMT) network have been growing to deal with different diseases via different sensors. These healthcare sensors are connecting to the various healthcare fog servers. The hospitals are geographically distributed and offer different services to the patients from any ubiquitous network. However, due to the full offloading of data to the insecure servers, two main challenges exist in the IoMT network. (i) Data security of workflows healthcare applications between different fog healthcare nodes. (ii) The cost-efficient and QoS efficient scheduling of healthcare applications in the IoMT system. This paper devises the Cost-Efficient Service Selection and Execution and Blockchain-Enabled Serverless Network for Internet of Medical Things system. The goal is to choose cost-efficient services and schedule all tasks based on their QoS and minimum execution cost. Simulation results show that the proposed outperform all existing schemes regarding data security, validation by 10%, and cost of application execution by 33% in IoMT.

Muhammad Attique Khan, Inzamam Mashood Nasir, Muhammad Sharif, Majed Alhaisoni · 7 authors

Wireless Capsule Endoscopy (WCE) is an imaging technology, widely used in medical imaging for stomach infection recognition. However, a one patient procedure takes almost seven to eight minutes and approximately 57,000 frames are captured. The privacy of patients is very important and manual inspection is time consuming and costly. Therefore, an automated system for recognition of stomach infections from WCE frames is always needed. An existing block chain-based approach is employed in a convolutional neural network model to secure the network for accurate recognition of stomach infections such as ulcer and bleeding. Initially, images are normalized in fixed dimension and passed in pre-trained deep models. These architectures are modified at each layer, to make them safer and more secure. Each layer contains an extra block, which stores certain information to avoid possible tempering, modification attacks and layer deletions. Information is stored in multiple blocks, i.e., block attached to each layer, a ledger block attached with the network, and a cloud ledger block stored in the cloud storage. After that, features are extracted and fused using a Mode value-based approach and optimized using a Genetic Algorithm along with an entropy function. The Softmax classifier is applied at the end for final classification. Experiments are performed on a private collected dataset and achieve an accuracy of 96.8%. The statistical analysis and individual model comparison show the proposed method’s authenticity.

Qian Min, Wang Shuting

In recent years, Blockchain technology has been mentioned for many times in the fields of finance, Internet and energy, and it is a distributed data ledger that is decentralized, authentic, reliable, traceable open and transparent. Blockchain is constantly figuring out how to integrate with other industries. This paper analyzes the current situation in the field of medical devices of China by combing the Annual Report on Medical Device Adverse Event Monitoring from 2013-2019, and using the compound annual average growth rate. The analysis found that this field has pain points such as lack of third-party regulation and difficulty in traceability, and then it proposed the specific application of blockchain technology in the field of medical devices to solve the problems in this field.

Ming Li, Leilei Zeng, Le Zhao, Renlin Yang · 6 authors

With the application of multimedia big data, the problems such as information leakage and data tampering have emerged. The security of images which is one of the most typical multimedia has become a major problem facing the large-scale open network environment. This paper proposed a blockchain-watermarking scheme to protect the privacy, integrity and availability of compressed sensed images, which effectively combines multimedia watermarking, compressed sensing, Interplanetary File System (IPFS) and blockchain technologies. Based on the reliable authentication of watermarking, the confidentiality protection of compressed sensing, the secure storage of IPFS, and the decentralization and non-tamperability of blockchain, the all-round security protection of the image big data based on compressive sensing can be realized. Experiments show that the proposed scheme is effective and feasible.

Alsehli Abrar, Wadood Abdul, Sanaa Ghouzali

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.

Hongchao Ma, Yi Man, Xiao Xing, Zihan Zhuo · 5 authors

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.

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.

Dunjie Zhang, Jinyin Chen, Xiaosong Lu

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.

Jeyasheela Rakkini Simon, K. Geetha

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.

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.

Guowei Zhang

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.

Shiqiang Zhang, Yang Cao, Zhenhu Ning, Fei Xue · 6 authors

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.

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.

Aishwarya Likhar, Rahul Agrawal, Sanjay Dorle

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.