Dalia Elwi, Osama Abu-Elnasr, A. S. Tolba, Samir Elmougy
Abstract Bitcoin is a digital cryptocurrency which had become the focus of scientific research in the modern era. Blockchain is the underlying technology of Bitcoin because of its decentralization, transparency, trust-less, and immutability features. However, blockchain can be considered the cause of Bitcoin scalability issues especially storage. Nodes in Bitcoin network need to store the full blockchain to validate transactions. By time, the blockchain size will be extremely huge. So, the full nodes will prefer to leave the network, and this leads to the blockchain being centralized and trusted. Therefore, security will be adversely affected. In this paper, we propose a Stateful Layered Chain Model which is based on storing accounts’ balances to reduce the size of the Bitcoin blockchain. This model changes the structure of the traditional blockchain from blocks to layers. The experimental results demonstrated that the proposed model reduces the size of blockchain by about 50.6%. Imlicitly, the transaction throughput can also be nearly doubled.
Syed Agha Hassnain Mohsan, Abdul Razzaq, Shahbaz Ahmed Khan Ghayyur, Hend Khalid Alkahtani · 6 authors
Several academicians have been actively contributing to establishing a practical solution to storing and distributing medical images and test reports in the research domain of health care in recent years. Current procedures mainly rely on cloud-assisted centralized data centers, which raise maintenance expenditure, necessitate a large amount of storage space, and raise privacy concerns when exchanging data across a network. As a result, it is critically essential to provide a framework that allows for the efficient exchange and storage of large amounts of medical data in a secure setting. In this research, we describe a unique proof-of-concept architecture for a distributed patient-centric test report and image management (PCRIM) system that aims to facilitate patient privacy and control without the need for a centralized infrastructure. We used an Ethereum blockchain and a distributed file system technology called the Inter-Planetary File System in this system (IPFS). Then, to secure a distributed and trustworthy access control policy, we designed an Ethereum smart contract termed the patient-centric access control protocol. The IPFS allows for the decentralized storage of medical metadata, such as images, with worldwide accessibility. We demonstrate how the PCRIM system design enables hospitals, patients, and image requestors to obtain patient-centric data in a distributed and secure manner. Finally, we tested the proposed framework in the Windows environment by deploying a smart contract prototype on an Ethereum TESTNET blockchain. The findings of the study indicate that the proposed strategy is both efficient and practicable.
Blockchain technology considers the central technology that is used within many applications used frequently with human life. And the primary core of the blockchain is the consensus algorithm which may affect the security of the chain as well as the required resource consumption which affect mainly the blockchain performance directly. In recent years many consensus algorithms have been used and proposed such as proof of work (PoW) and proof of stake (PoS) and many others. However, these algorithms still need some improvement to the security and system resource consumption which will reduce the need for a huge amount of energy and save the environment as well as let the blockchain be useable within low computation ability devices such as the internet of things devices (IoT). This paper proposes a new consensus algorithm that ensures the integrity and authorization of nodes participating in the validation of the transaction and only a predefined number of nodes chosen randomly to participate in block addition which reduces the need for high computations power for mining and voting. The proposed algorithm needs lower time and computation costs comparable to the standard POW algorithm.
Anand Singh Rajawat, S. B. Goyal, Pradeep Bedi, Simeon Simoff · 6 authors
Large-scale clinical information sharing (CIS) provides significant advantages for medical treatments, including enhanced service standards and accelerated scheduling of health services. The current CIS suffers many challenges such as data privacy, data integrity, and data availability across multiple healthcare institutions. This study introduces an innovative blockchain-based electronic healthcare system that incorporates synchronous data backup and a highly encrypted data-sharing mechanism. Blockchain technology, which eliminates centralized organizations and reduces the number of fragmented patient files, could make it easier to use machine learning (ML) models for predictive diagnosis and analysis. In turn, it might lead to better medical care. The proposed model achieved an improved patient-centered CIS by personalizing the separation of information with an intelligent ”allowed list“ for clinician data access. This work introduces a hybrid ML-blockchain solution that combines traditional data storage and blockchain-based access. The experimental analysis evaluated the proposed model against the competing models in comparative and quantitative studies in large-scale CIS examples in terms of model viability, stability, protection, and robustness, with improved results.
A. B. Pawar, M.A. Jawale, P. William, Gurpreet Singh Chhabra · 7 authors
Early identification of lung cancer is essential since the disease progresses quickly. Early-stage lung cancer diagnosis will be the first usage of the Internet of Things (IoT). With a worldwide network of IoT devices and a high degree of trust in the model's accuracy, on-the-fly training for IoT devices is very essential. As many as a million lives are saved each year because to early detection of illness, which seals the airways and prevents infection. Image processing and machine learning techniques provided the first evidence of malignant growth. Symptoms of lung cancer generally don't show up until the disease has advanced very far. At this stage, getting medical help becomes quite difficult. A whistling sound, hoarseness, weight gain in the face and/or an increase in the size of the upper chest may appear first, followed by the curling or rising of your fingers or the experience of pain when swallowing. Sputum with a red or rust-colored hue is a sign of malignancy, as is shortness of breath and chronic chest pain. In addition to identifying and arranging lung knobs, a lung computed tomography image may also be utilised to estimate their risk level. Preparation does not have as much of an impact on ECNN's accuracy and temporal complexity as it did on previous frameworks. They are made up of abnormal cells that form a tumour. An uncontrolled development and destruction of the lungs. Various kinds of lung cancer begin to develop as a result of this process, which continues until a tumour forms. Lung cells are damaged when they come into contact with airborne contaminants. + The new approach offered is ECNN+.
Given their strong performance on a variety of graph learning tasks, Graph Neural Networks (GNNs) are increasingly used to model financial networks. Traditional GNNs, however, are not able to capture higher-order topological information, and their performance is known to degrade with the presence of negative edges that may arise in many common financial applications. Considering the rich semantic inference of negative edges, excluding them as an obvious solution is not elegant. Alternatively, another basic approach is to apply positive normalization, however, this also may lead to information loss. Our work proposes a simple yet effective solution to overcome these two challenges by employing the eigenvectors with top-k largest eigenvalues of the raw adjacency matrix for pre-embeddings. These pre-embeddings contain high-order topological knowledge together with the information on negative edges, which are then fed into a GNN with a positively normalized adjacency matrix to compensate for its shortcomings. Through comprehensive experiments and analysis, we empirically demonstrate the superiority of our proposed solution in a Bitcoin user reputation score prediction task.
K. Kalyani, Velmurugan Subbiah Parvathy, Hikmat A. M. Abdeljaber, T. Satyanarayana Murthy · 7 authors
In recent times, financial globalization has drastically increased in different ways to improve the quality of services with advanced resources. The successful applications of bitcoin Blockchain (BC) techniques enable the stockholders to worry about the return and risk of financial products. The stockholders focused on the prediction of return rate and risk rate of financial products. Therefore, an automatic return rate bitcoin prediction model becomes essential for BC financial products. The newly designed machine learning (ML) and deep learning (DL) approaches pave the way for return rate predictive method. This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder (JSO-ELMAE) for return rate prediction of BC financial products. The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products. Besides, the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results. The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates. The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects. The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.
Blockchain is one of the most advanced technologies that play an important role in many different fields such as healthcare, capital markets and logistics. Among the many existing blockchain platforms, the integration of the Turingcomplete virtual programming engine with the blockchain makes the Ethereum blockchain one of the most paramount infrastructures for various types of applications, including but not limited to cryptocurrency trading, smart contracts, decentralised finance and metaverse. Nevertheless, Ethereum like many other computing systems, has fallen victim to vector attacks that exploit its vulnerabilities and have catastrophic consequences. Out of the need to protect Ethereum from such attacks, this paper proposes a novel deep learning model based on convolutional neural networks. The proposed model treats the transaction, which is the atomic entity in this platform, as a stochastic time series and then develops two specific task layers that are compatible with the traditional CNN architecture. The first layer is responsible for detecting the seasonal characteristics of the transactions, while the second layer is used for detecting the trend. These two layers are integrated with the traditional architecture to form a powerful temporal CNN architecture that can classify different types of attacks. The performance of the proposed model was evaluated from a different perspective using real transactions collected from the Ethereum main-net network. The results of the comprehensive evaluations show the ability of the proposed model to perfectly identify malicious transactions in the Ethereum blockchain.
Aiming to solve the problems of low fault tolerance, low throughput, and high delay in traditional methods, an improved method of the blockchain cross-chain consensus algorithm based on weighted PBFT is proposed. This article constructs a blockchain cross-chain exchange model based on cluster centers and divides the nodes in the blockchain system into consensus service nodes, cross-chain exchange nodes, and application nodes to improve the performance of consensus computing services. On this basis, according to the weighted PBFT consensus mechanism, the blockchain consensus environment is set up, and the distribution of nodes in the consensus domain and the blockchain signature scheme are obtained. Therefore, the blockchain cross-chain consensus optimization algorithm is designed to reduce throughput and delay and optimize the consensus effect. The experimental results show that the proposed method can effectively improve the shortcomings of traditional methods, with high throughput and low latency, and strong security. It shows that it is a low resource consumption and secure consensus method.
Tao Hai, Jincheng Zhou, S. Srividhya, Sanjiv Jain · 6 authors
Abstract Blockchain is the latest boon in the world which handles mainly banking and finance. The blockchain is also used in the healthcare management system for effective maintenance of electronic health and medical records. The technology ensures security, privacy, and immutability. Federated Learning is a revolutionary learning technique in deep learning, which supports learning from the distributed environment. This work proposes a framework by integrating the blockchain and Federated Deep Learning in order to provide a tailored recommendation system. The work focuses on two modules of blockchain-based storage for electronic health records, where the blockchain uses a Hyperledger fabric and is capable of continuously monitoring and tracking the updates in the Electronic Health Records in the cloud server. In the second module, LightGBM and N-Gram models are used in the collaborative learning module to recommend a tailored treatment for the patient’s cloud-based database after analyzing the EHR. The work shows good accuracy. Several metrics like precision, recall, and F1 scores are measured showing its effective utilization in the cloud database security.
This paper is an attempt to find the energy required for the comminution of fault zone rocks and also to determine the energy required to grind ore from infinite size to the desired particle size in non-traditional approach, for various value additions. The results in the present investigations also confirm about the brittleness test and friability tests, whose values depend on the drop weight and its height for different types of fault zone rock. Also the results of its brittleness tests determine the grindability of fault zone rocks. All the outcome results are then secured with the help of decentralized and immutable record-keeping system using Blockchain technology. The Blockchain network in the present investigations not only allows any users to enhance the performance but also it will secure the experimental outcomes in immutable distributed ledgers through smart contracts to increase transparency between users in a trusted manner.
Discover the intersectionality of Decentralized Identifiers (DIDs) and Non-Fungible Token (NFTs) usage within the Ethereum blockchain by analyzing Ethereum and NFT platforms to provide as much qualitative and quantitative context as possible.
Puja S. Prasad, G. N. Beena Bethel, Ninni Singh, Vinit Kumar Gunjan · 6 authors
Medical image analysis technology based on deep learning has played an important role in computer-aided disease diagnosis and treatment. Classification accuracy has always been the primary goal pursued by researchers. However, the image transmission process also faces the problems of limited wireless ad-hoc network (WAN) bandwidth and increased security risks. Moreover, when user data are exposed to unauthorized users, platforms can easily leak personal privacy. Aiming at the abovementioned problems, a system model and an access control scheme for the collaborative analysis of the diagnosis of diabetic retinopathy (DR) are constructed in this paper. The system model includes two stages of data cleaning and lesion classification. In the data cleaning phase, the private cloud writes the model obtained after training into the blockchain, and other private clouds use the best-performing model on the chain to identify the image quality when cleaning data and pass the high-quality image to the lesion classification model for use. In the lesion classification stage, each private cloud trains the classification model separately; uploads its own model parameters to the public cloud for aggregation to obtain a global model; and then sends the global model to each private cloud to achieve collaborative learning, reduce the amount of data transmission, and protect personal privacy. Access control schemes include improved role-based access control (RAC) used within the private cloud and blockchain-based access control used during the interaction between the private cloud and the public cloud program (BAC). RAC grants both functional rights and data access rights to roles and takes into account object attributes for fine-grained level control. Based on certificateless public-key encryption technology and blockchain technology, BAC can realize the identity authentication and authority identification of the private cloud while requesting the transmission of model parameters from the private cloud to the public cloud and protect the security of the identity, authority, and model parameters of the private cloud to achieve the effect of lightweight access control. In the experimental part, two retinal datasets are used for DR classification analysis. The results show that data cleaning can effectively remove low-quality images and improve the accuracy of early lesion classification for doctors, with an accuracy rate of 90.2%.
Abstract:The DApp (Decentralised application) being developed enables easy verification of credentials by storing the certificates on Ethereum blockchain network using IPFS (Inter Planetary File System) which is a distributed file system, thereby making the information stored immutable and secure. The website is being developed in three phases. In the first phase, the college enrolls students and uploads their credentials on the Ethereum blockchain. In the second phase, students can view their credentials and access requests sent by companies. In the third phase, companies can send access requests to students whose credentials they want to verify. Once the students accept the access requests, companies can view and verify the certificates.
In today's healthcare environment, incorporating cutting-edge technology is crucial to tackle the increasing difficulties and guarantee effective patient care. Comprehensive healthcare information relies heavily on multimedia data, including various sources such as photos, videos, and sensor data. This paper explores the importance of Multimedia Data Processing and Analysis in healthcare and emphasizes the need for creative frameworks to manage this data efficiently. The current solutions need help with security, transparency, and interoperability, therefore requiring a fundamental change in approach. This study introduces the Hybrid Blockchain Framework for IoT-Healthcare Application (HDF-IoT-HA), which combines web-based communication, dual networks consisting of miners and execution nodes, and a hybrid blockchain system. The structure places a high emphasis on ensuring that data interactions between patients and medical professionals are both safe and transparent. The simulation results demonstrate the impressive capabilities of HDF-IoT-HA, including an average Transaction Efficiency of 97.63%, a reduction in latency of 9.82 ms, an improvement in system reliability of 27.46%, a security rating of 95.66%, and an extended network lifetime of 135.11 hours. These results highlight the framework's effectiveness in improving communication in healthcare, maintaining data security, and strengthening the dependability of systems in IoT-enabled medical applications.
In teleradiology, medical images are transmitted to offsite radiologists for interpretation and the dictation report is sent back to the original site to aid timely diagnosis and proper patient care. Although teleradiology offers great benefits including time and cost efficiency, after-hour coverages, and staffing shortage management, there are some technical and operational limitations to overcome in reaching its full potential. We analyzed the current teleradiology workflow to identify inefficiencies. Image unavailability and delayed critical result communication stemmed from lack of system integration between teleradiology practice and healthcare institutions are among the most substantial factors causing prolonged turnaround time. In this paper, we propose a blockchain-based medical image sharing and automated critical-results notification platform to address the current limitation. We believe the proposed platform will enhance efficiency in workflow by eliminating the need for intermediaries and will benefit patients by eliminating the need for storing medical images in hard copies. While considerable progress was achieved, further research on governance and HIPAA compliance is required to optimize the adoption of the new application. Towards an idea to a working paradigm, we will implement the prototype during the next phase of our study.
The current development of blockchain, technically speaking, still faces many key problems such as efficiency and scalability issues, and any distributed system faces the problem of how to balance consistency, availability, and fault tolerance need to be solved urgently. The advantage of blockchain is decentralization, and the most important thing in a decentralized system is how to make nodes reach a consensus quickly. This research mainly discusses the blockchain and K-means algorithm for edge AI computing. The natural pan-central distributed trustworthiness of blockchain provides new ideas for designing the framework and paradigm of edge AI computing. In edge AI computing, multiple devices running AI algorithms are scattered across the edge network. When it comes to decentralized management, blockchain is the underlying technology of the Bitcoin system. Due to its characteristics of immutability, traceability, and consensus mechanism of transaction data storage, it has recently received extensive attention. Blockchain technology is essentially a public ledger. This is done by recording data related to trust management to this ledger. To collaboratively complete artificial intelligence computing tasks or jointly make intelligent group decisions, frequent communication is required between these devices. By integrating idle computing resources in an area, a distributed edge computing platform is formed. Users obtain benefits by sharing their computing resources, and nodes in need complete computing tasks through the shared platform. In view of the identity security problems faced in the sharing process, this article introduces blockchain technology to realize the trust between users. All participants must register a secure identity in the blockchain network and conduct transactions in this security system. A K-means algorithm suitable for edge environments is proposed to identify different degradation stages of equipment operation reflected by multiple types of data. Based on the prediction of the fault state for a single type of data, the algorithm uses the historical data of multiple types of data together with the prediction data to predict the fault stage. During the research process, the average optimization energy consumption of K-means algorithm is 14.6% lower than that of GA. On the basis of designing a resource allocation scheme based on blockchain, the problem of how the participants can realize reliable resource use according to the recorded data on the chain is studied. The article implements the verification of the legality of the use of blockchain resources. In addition, a control node is introduced to master the global real-time information of the network to provide data support for the user's choice.
As Blockchain is a distributed digital ledger system, it focuses on various sectors such as bitcoin, the banking sector, the corporation sector, the real estate and the healthcare sector. Each block in the blockchain contains the hash value, timestamp and transaction data of their previous block. The consensus algorithms plays a major role in the blockchain framework. This consensus algorithm maintaining the safety and efficacy of blockchain. The consensus protocols determines how the agreement to add the updated block to all nodes in the network works. Every consensus protocols has its own set of performance and scalability features. It is essential to technically compare each consensus mechanism by highlighting their strengths and weaknesses. Consensus algorithms in blockchain can be divided into two types. They are Proof based consensus and voting based consensus. The Proof based consensus shows that they are more qualified than others to do mining work. Voting-based consensus explains that nodes are needed in a blockchain network to exchange decisions for mining a new block or transaction before reaching a final conclusion. Their effectiveness can be enhanced by manipulating the suitable consensus algorithm in the blockchain. Blockchain technology is recently implement in many domains, especially for healthcare Industry.
Blockchain technology is gaining a lot of attention in various fields, such as intellectual property, finance, smart agriculture, etc. The security features of blockchain have been widely used, integrated with artificial intelligence, Internet of Things (IoT), software defined networks (SDN), etc. The consensus mechanism of blockchain is its core and ultimately affects the performance of the blockchain. In the past few years, many consensus algorithms, such as proof of work (PoW), ripple, proof of stake (PoS), practical byzantine fault tolerance (PBFT), etc., have been designed to improve the performance of the blockchain. However, the high energy requirement, memory utilization, and processing time do not match with our actual desires. This paper proposes the consensus approach on the basis of PoW, where a single miner is selected for mining the task. The mining task is offloaded to the edge networking. The miner is selected on the basis of the digitization of the specifications of the respective machines. The proposed model makes the consensus approach more energy efficient, utilizes less memory, and less processing time. The improvement in energy consumption is approximately 21% and memory utilization is 24%. Efficiency in the block generation rate at the fixed time intervals of 20 min, 40 min, and 60 min was observed.
Md. Rafiqul Islam, Muhammad Mahbubur Rahman, Mohammed Ataur Rahman, Muslim Har Sani Mohamad · 5 authors
The alternative energy generation sources have increased drastically from centralized systems to distributed systems which increases the stability of energy distribution management systems and reduces the distribution cost as well. On the other hand, it reduces the probability of major area electricity blackout chances and decreases the energy distribution loss. For proper distribution and management of energy, there are different types of advanced technologies like artificial intelligence, and the Internet of Things (IoT) available, but a blockchain automated system is one of the best choices and is highly recommended. Various aspects of blockchain technology and energy management system have been discussed in this review paper where a total number of 423 journal papers, articles, and online information sources have been reviewed in the initial stage, and finally, 63 published research articles have been selected for review. There are several topics, including technology overview in energy management systems, blockchain application of energy trading, blockchain technology implementation challenges, distributed energy management system with Ethereum, and a conclusion with some recommendations have been discussed. Blockchain and Distributed Ledger Technology (DLT) are highly transparent, authenticate, and secure systems that can be used for distributing the energy between distributor and consumer without an intermediator which increases the overall efficiency of the system. This paper aims to highlight the blockchain and distributed ledger technology and how it works as well as optimize the transaction processing cost among the participants of the consortium network. This paper will make a significant contribution to the new research work and in the field of energy management systems.
Blockchain mining is a power &resource consuming task, which requires multiple-levels of optimization, both at resource &task level. Over the years, a wide variety of mining optimization models are proposed by researchers, but most of them are applicable only to a subset of mining types. For instance, mining models used for Proof-of-Work (PoW) consensus-based mining, are not applicable for Delegated Proof-of-Stake (DPoS), and other consensus types. This limits the scalability of these models, which reduces their adoptability for dynamic blockchain systems (DBSes). These DBSes utilize different consensus models as per context of data storage, and are widely used by blockchain designers to deploy high-efficiency, and low delay storage solutions. A standard mining optimization solution is not available for such scenarios, due to which researchers & system designers opt for deployment-specific optimizations, which need to be redesigned for each blockchain system. To remove this drawback, a standard blockchain mining optimization model is proposed in this text. This model uses a combination of Genetic Algorithm (GA) & Particle Swarm Optimization (PSO) for solving two different issues. The GA model is used to optimize miner set selection, which will be used for consensus, while the PSO model optimizes the responses from these miner sets depending upon their temporal mining performance. Due to optimum miner set selection, only higher efficiency miner nodes are used for mining the blockchain. While due to performance optimization of these miner nodes, their internal mining efficiency is improved.This efficiency is evaluated in terms of delay & power needed for single block mining w.r.t. blockchain length. It was observed that a combination of these models is capable of enhancing mining speed, with reduced power consumption, and higher mining throughput. Due to this improvement the proposed HBSBA model outperforms most of the recently proposed blockchain mining models. The model was evaluated on DPoS, Proof-of-Authority (PoA), Proof-of-Stake (PoS), and PoW based consensus models, and a delay reduction of 14.5%, throughput improvement of 8.3%, and reduction in energy consumption by 4.6% when compared with various state-of-the-art models. Due to this improvement, the proposed model is applicable for a wide variety of medium to large scaled blockchain mining applications.
Rana Fareed Ghani, Asia A. Salman, Abdullah B. Khudhair, Laith Al-Jobouri
One of the major capabilities of blockchain technology is the sharing of data in verifiable ways without losing control of information possession. Issuing and verifying student certifications for higher study applications or job recruitment require many steps that take days to complete and are considered time-consuming. Most universities around the world use centralized systems to control the entire procedure when a graduate applies for a job or postgraduate studies. Applying blockchain technology to certificate verification protocols through a comprehensive architecture provides authenticity and reduces time significantly. In this paper, a framework has been proposed to issue student certifications locally in addition to sharing them across the internet while maintaining control and ownership of the certifications. This framework leverages the advantages of blockchain technology to electronic certification sharing and verification. Applying the proposed blockchain-based certification system in universities will provide low latency for issuing, sharing, and verification of these certifications. The paper presents the proposed blockchain-based framework for e-certification sharing and an evaluation of the framework, which consists of measuring the average time to issue a certificate and transaction latency time.
Kai Chen, Cheng Xu, Hongzhe Liu, Pengfei Wang · 5 authors
The development of 5G network communication has brought technological innovation to smart city communication, making the realization of V2X (vehicle to everything) technology possible. Vehicles wirelessly communicate with other vehicles, sensors, pedestrians, and roadside units, raising data security issues while driving. In order to ensure driving safety, the risk map cognitive model is established with the help of blockchain technology. In this model, the key map data and personal privacy information are encrypted and uploaded to form a blockchain, and the smart contract technology is used for automatic script processing. Then, according to different risk scenarios, cognitive learning is carried out for different risk levels, the cognitive results and corresponding operations are fed back to the intelligent vehicle, and these operations ensure the safe operation of the vehicle according to the intelligent vehicle. Finally, the feasibility of the model was verified by comparing different dangerous scenarios. The experimental results show that this risk cognition model can cognize the data of the intelligent vehicle according to different danger scenarios, and the model can transmit acceleration, deceleration, braking, and other behaviors to the intelligent vehicle to ensure smart city driving safety.
Feng Liu, Chengyi Yang, Jie Yang, Deli Kong · 7 authors
As a distributed storage scheme, the blockchain network lacks storage space has been a long-term concern in this field. At present, there are relatively few research on algorithms and protocols to reduce the storage requirement of blockchain, and the existing research has limitations such as sacrificing fault tolerance performance and raising time cost, which need to be further improved. Facing the above problems, this paper proposes a protocol based on Distributed Image Storage Protocol (DISP), which can effectively improve blockchain storage space and reduces computational costs in the help of InterPlanetary File System (IPFS). In order to prove the feasibility of the protocol, we make full use of IPFS and distributed database to design a simulation experiment for blockchain. Through distributed pooling (DP) algorithm in this protocol, we can divide image evidence into recognizable several small files and stored in several nodes. And these files can be restored to lossless original documents again by inverse distributed pooling (IDP) algorithm after authorization. These advantages in performance create conditions for large scale industrial and commercial applications.