Research and Implementation of Credit Investigation Sharing Platform Based on Double Blockchain

As the development of the modern economy is increasingly inseparable from credit support, the traditional credit investigation mode has yet to meet this demand. Because of the difficulties in conventional credit data sharing among credit investigation agencies, poor data portability, and centralized supervision, this paper proposes a data-sharing scheme for credit investigation agencies based on a double blockchain. Given the problems such as difficult data sharing, difficult recovery of damaged data, and accessible data leakage between institutions and users with non-traditional credit investigation data other than credit, this paper proposes a data-sharing scheme for credit investigation subjects based on the digital envelope. Based on the above two solutions, this paper designs a double blockchain credit data-sharing platform based on the “public chain + alliance chain” from credit investigation agencies’ and visiting subjects’ perspectives. The sharing platform uses the alliance chain as the management chain to solve the problem of complex data sharing between credit bureaus and centralized supervision, uses the public chain as the use chain to solve the problem of complex data sharing between the access subject and the credit bureaus, uses the interplanetary file system and digital envelope and other technologies to solve the problem of difficult recovery of damaged data, data leakage, and other issues. After the upload test, the average upload speed reaches 80.6 M/s. The average download speed of the system is 88.7 M/s after the download test. The multi-thread stress test tests the linkage port on the system package, and the average response time for the hypertext transfer protocol (HTTP) is 0.6 ms. The system performance and security analysis show that the sharing platform can provide safe and reliable credit-sharing services for organizations and users and high working efficiency.     

A Key Recovery System Based on Password-Protected Secret Sharing in a Permissioned Blockchain

In today’s fourth industrial revolution, various blockchain technologies are being actively researched. A blockchain is a peer-to-peer data-sharing structure lacking central control. If a user wishes to access stored data, she/he must employ a private key to prove ownership of the data and create a transaction. If the private key is lost, blockchain data cannot be accessed. To solve such a problem, public blockchain users can recover the key using a wallet program. However, key recovery in a permissioned blockchain (PBC) has been but little studied. The PBC server is Honest-but-Curious (HBC), and should not be able to learn anything of the user; the server should simply recover and store the key. The server must also be resistant to malicious attacks. Therefore, key recovery in a PBC must satisfy various security requirements. Here, we present a password-protected secret sharing (PPSS) key recovery system, protected by a secure password from a malicious key storage server of a PBC. We describe existing key recovery schemes and our PPSS scheme.     

Towards Privacy-Preserving Cloud Storage: A Blockchain Approach

With the rapid development of cloud computing technology, cloud services have now become a new business model for information services. The cloud server provides the IT resources required by customers in a self-service manner through the network, realizing business expansion and rapid innovation. However, due to the insufficient protection of data privacy, the problem of data privacy leakage in cloud storage is threatening cloud computing. To address the problem, we propose BC-PECK, a data protection scheme based on blockchain and public key searchable encryption. Firstly, all the data is protected by the encryption algorithm. The privacy data is encrypted and stored in a cloud server, while the ciphertext index is established by a public key searchable encryption scheme and stored on the blockchain. Secondly, based on the characteristics of trusted execution of smart contract technology, a control mechanism for data accessing and sharing is given. Data transaction is automatically recorded on the blockchain, which is fairer under the premise of ensuring the privacy and security of the data sharing process. Finally, we analyzed the security and fairness of the current scheme. Through the comparison with similar schemes, we have shown the advantages of the proposed scheme.     

基于区块链技术的空调产品电子病历共享方案

随着空调领域大数据和智能化的飞速发展,良好的数据管理模式变得十分重要.本文首次提出一种将区块链作为底层技术的空调产品电子病历共享方案,旨在构建一个去中心化和第三方信任的空调病历信息共享平台.通过运用哈希计算、非对称加密和全网共识等技术,达到对空调故障病历数据的全网可信,突破个人、企业之间的信任壁垒.研究表明:基于区块链...     

基于本体的矿产资源空间信息共享系统的研究

随着SemanticWeb技术的发展,基于Ontology的空间信息服务是分布式网络环境下异构信息资源共享与互操作实现的可行途径。在对基于Geo-Ontology的信息服务实现机制、构建策略及服务流程进行深入探索的基础上,结合目前主流商品化GIS软件,提出实现基于Geo-Ontology的矿产资源空间信息共享与互操作方案。方案的构建将分散在Internet上的矿产资源信息系统开放和动态地集成在一起,实现分布式环境下异构矿产资源空间信息的共享与互操作。     

Federated Learning Based on Data Divergence and Differential Privacy inFinancial Risk Control Research

In the financial sector, data are highly confidential and sensitive, and ensuring data privacy is critical. Sample fusion is the basis of horizontal federation learning, but it is suitable only for scenarios where customers have the same format but different targets, namely for scenarios with strong feature overlapping and weak user overlapping. To solve this limitation, this paper proposes a federated learning-based model with local data sharing and differential privacy. The indexing mechanism of differential privacy is used to obtain different degrees of privacy budgets, which are applied to the gradient according to the contribution degree to ensure privacy without affecting accuracy. In addition, data sharing is performed to improve the utility of the global model. Further, the distributed prediction model is used to predict customers’ loan propensity on the premise of protecting user privacy. Using an aggregation mechanism based on federated learning can help to train the model on distributed data without exposing local data. The proposed method is verified by experiments, and experimental results show that for non-iid data, the proposed method can effectively improve data accuracy and reduce the impact of sample tilt. The proposed method can be extended to edge computing, blockchain, and the Industrial Internet of Things (IIoT) fields. The theoretical analysis and experimental results show that the proposed method can ensure the privacy and accuracy of the federated learning process and can also improve the model utility for non-iid data by 7% compared to the federated averaging method (FedAvg).     

Blockchain Technology Based Information Classification Management Service

Hyper-connectivity in Industry 4.0 has resulted in not only a rapid increase in the amount of information, but also the expansion of areas and assets to be protected. In terms of information security, it has led to an enormous economic cost due to the various and numerous security solutions used in protecting the increased assets. Also, it has caused difficulties in managing those issues due to reasons such as mutual interference, countless security events and logs’ data, etc. Within this security environment, an organization should identify and classify assets based on the value of data and their security perspective, and then apply appropriate protection measures according to the assets’ security classification for effective security management. But there are still difficulties stemming from the need to manage numerous security solutions in order to protect the classified assets. In this paper, we propose an information classification management service based on blockchain, which presents and uses a model of the value of data and the security perspective. It records transactions of classifying assets and managing assets by each class in a distributed ledger of blockchain. The proposed service reduces assets to be protected and security solutions to be applied, and provides security measures at the platform level rather than individual security solutions, by using blockchain. In the rapidly changing security environment of Industry 4.0, this proposed service enables economic security, provides a new integrated security platform, and demonstrates service value.     

Deletion and Recovery Scheme of Electronic Health Records Based on Medical Certificate Blockchain

The trusted sharing of Electronic Health Records (EHRs) can realize the efficient use of medical data resources. Generally speaking, EHRs are widely used in blockchain-based medical data platforms. EHRs are valuable private assets of patients, and the ownership belongs to patients. While recent research has shown that patients can freely and effectively delete the EHRs stored in hospitals, it does not address the challenge of record sharing when patients revisit doctors. In order to solve this problem, this paper proposes a deletion and recovery scheme of EHRs based on Medical Certificate Blockchain. This paper uses cross-chain technology to connect the Medical Certificate Blockchain and the Hospital Blockchain to realize the recovery of deleted EHRs. At the same time, this paper uses the Medical Certificate Blockchain and the InterPlanetary File System (IPFS) to store Personal Health Records, which are generated by patients visiting different medical institutions. In addition, this paper also combines digital watermarking technology to ensure the authenticity of the restored electronic medical records. Under the combined effect of blockchain technology and digital watermarking, our proposal will not be affected by any other rights throughout the process. System analysis and security analysis illustrate the completeness and feasibility of the scheme.     

Blockchain-based Distributed Power Market Trading Mechanism

Distributed power market trading has the characteristics of large number of participants, scattered locations, small single trading scale, and point-to-point trading. The traditional centralized power trading model has the problems of large load, low efficiency, high cost, reliance on third parties and unreliable data. With the characteristics of decentralization and non-tampering, blockchain can establish a point-to-point trusted trading environment and provide effective solutions to the above problems. Therefore, this paper proposed a distributed power market trading framework based on blockchain. In this framework, the distributed power supply characteristics and trading needs of each participant are analyzed, a complete distributed trading process based on blockchain is designed. In addition, we have studied the key technologies of distributed power market trading. With the goal of power service reputation and maximum revenue of distributed power providers, we have established a matching degree model, a distributed power market trading optimization model, and designed a smart contract-based power market trading optimization strategy and power trading settlement strategy. Finally, we designed experiments to verify the performance of the proposed framework.     

Secure and Efficient Data Storage and Sharing Scheme Based on Double Blockchain

In the digital era, electronic medical record (EMR) has been a major way for hospitals to store patients’ medical data. The traditional centralized medical system and semi-trusted cloud storage are difficult to achieve dynamic balance between privacy protection and data sharing. The storage capacity of blockchain is limited and single blockchain schemes have poor scalability and low throughput. To address these issues, we propose a secure and efficient medical data storage and sharing scheme based on double blockchain. In our scheme, we encrypt the original EMR and store it in the cloud. The storage blockchain stores the index of the complete EMR, and the shared blockchain stores the index of the shared part of the EMR. Users with different attributes can make requests to different blockchains to share different parts according to their own permissions. Through experiments, it was found that cloud storage combined with blockchain not only solved the problem of limited storage capacity of blockchain, but also greatly reduced the risk of leakage of the original EMR. Content Extraction Signature (CES) combined with the double blockchain technology realized the separation of the privacy part and the shared part of the original EMR. The symmetric encryption technology combined with Ciphertext-Policy Attribute-Based Encryption (CP–ABE) not only ensures the safe storage of data in the cloud, but also achieves the consistency and convenience of data update, avoiding redundant backup of data. Safety analysis and performance analysis verified the feasibility and effectiveness of our scheme.     

Privacy Data Management Mechanism Based on Blockchain and Federated Learning

Due to the extensive use of various intelligent terminals and the popularity of network social tools, a large amount of data in the field of medical emerged. How to manage these massive data safely and reliably has become an important challenge for the medical network community. This paper proposes a data management framework of medical network community based on Consortium Blockchain (CB) and Federated learning (FL), which realizes the data security sharing between medical institutions and research institutions. Under this framework, the data security sharing mechanism of medical network community based on smart contract and the data privacy protection mechanism based on FL and alliance chain are designed to ensure the security of data and the privacy of important data in medical network community, respectively. An intelligent contract system based on Keyed-Homomorphic Public Key (KH-PKE) Encryption scheme is designed, so that medical data can be saved in the CB in the form of ciphertext, and the automatic sharing of data is realized. Zero knowledge mechanism is used to ensure the correctness of shared data. Moreover, the zero-knowledge mechanism introduces the dynamic group signature mechanism of chosen ciphertext attack (CCA) anonymity, which makes the scheme more efficient in computing and communication cost. In the end of this paper, the performance of the scheme is analyzed from both asymptotic and practical aspects. Through experimental comparative analysis, the scheme proposed in this paper is more effective and feasible.     

Application of blockchain in the field of intelligent manufacturing: Theoretical basis,realistic plights,and development suggestions

Blockchain technology is considered one of the promising technologies of the information technology era. The core features of blockchain, such as decentralization, transparency, high security, and tamper-proof nature, bring great convenience for large-scale social cooperation and data sharing. Blockchain has a broad application prospect in the field of intelligent manufacturing. The key issues of this field, such as distributed collaborative production, industrial big data sharing and security, transparent logistics, and supply chain, are naturally consistent with the core characteristics of the blockchain technology. This study aims to analyze the application of blockchain in the field of intelligent manufacturing. First, we introduce the basic connotation and applications of blockchain. Then, we propose the theoretical basis for the application of blockchain in the field of intelligent manufacturing. Finally, we point out the realistic plights and provide some suggestions to promote the application of blockchain in the field of intelligent manufacturing.     

Distributed Secure Storage Scheme Based on Sharding Blockchain

Distributed storage can store data in multiple devices or servers to improve data security. However, in today's explosive growth of network data, traditional distributed storage scheme is faced with some severe challenges such as insufficient performance, data tampering, and data lose. A distributed storage scheme based on blockchain has been proposed to improve security and efficiency of traditional distributed storage. Under this scheme, the following improvements have been made in this paper. This paper first analyzes the problems faced by distributed storage. Then proposed to build a new distributed storage blockchain scheme with sharding blockchain. The proposed scheme realizes the partitioning of the network and nodes by means of blockchain sharding technology, which can improve the efficiency of data verification between nodes. In addition, this paper uses polynomial commitment to construct a new verifiable secret share scheme called PolyVSS. This new scheme is one of the foundations for building our improved distributed storage blockchain scheme. Compared with the previous scheme, our new scheme does not require a trusted third party and has some new features such as homomorphic and batch opening. The security of VSS can be further improved. Experimental comparisons show that the proposed scheme significantly reduces storage and communication costs.     

Privacy Preserving Blockchain Technique to Achieve Secure and Reliable Sharing of IoT Data

In present digital era, an exponential increase in Internet of Things (IoT) devices poses several design issues for business concerning security and privacy. Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT. In this view, this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine (ACOMKSVM) with Elliptical Curve cryptosystem (ECC) for secure and reliable IoT data sharing. This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM training algorithms in partial views of IoT data, collected from various data providers. Then, ECC is used to create effective and accurate privacy that protects ACOMKSVM secure learning process. In this study, the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers, where IoT data is encrypted and recorded in a distributed ledger. The security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMKSVM model for data analysts. To examine the performance of the proposed method, it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set (BCWD) and Heart Disease Data Set (HDD) from UCI AI repository. The simulation outcome indicated that the ACOMKSVM model has outperformed all the compared methods under several aspects.     

Energy-Efficient and Blockchain-Enabled Model for Internet of Things (IoT) in Smart Cities

Wireless sensor networks (WSNs) and Internet of Things (IoT) have gained more popularity in recent years as an underlying infrastructure for connected devices and sensors in smart cities. The data generated from these sensors are used by smart cities to strengthen their infrastructure, utilities, and public services. WSNs are suitable for long periods of data acquisition in smart cities. To make the networks of smart cities more reliable for sensitive information, the blockchain mechanism has been proposed. The key issues and challenges of WSNs in smart cities is efficiently scheduling the resources; leading to extending the network lifetime of sensors. In this paper, a linear network coding (LNC) for WSNs with blockchain-enabled IoT devices has been proposed. The consumption of energy is reduced for each node by applying LNC. The efficiency and the reliability of the proposed model are evaluated and compared to those of the existing models. Results from the simulation demonstrate that the proposed model increases the efficiency in terms of the number of live nodes, packet delivery ratio, throughput, and the optimized residual energy compared to other current techniques.     

Examination of effect of information sharing on businesses performance in the supply chain process

Nowadays, supply chain management (SCM) has become one of the focal points of competition. The importance of information sharing in SCM is gradually increasing in terms of improving the business performance. In this study, an attempt to determine the effect of information sharing in the supply chain process (SCP) on cost, financial and business performance, in general, was made. The attempt was made by means of a measuring tool which is based on a theoretical approach. For this purpose, the structural equation model (SEM) was established by taking into account the supply chain structure, SCP, supply chain flexibility, environmental uncertainties and information sharing latent structures in businesses and the cost and financial performance indicators. In this study, differently from most other studies, two performance variables were included, and the model was created by defining these variables, which could be measured with more than one indicator, as latent variables. For this purpose, the data obtained from a questionnaire study performed on large firms within the scope of ISO 1000 were used. An attempt to reveal the factors affecting the cost and financial performance of businesses in SCM was made in the study. As a result of the analyses performed, the proposed SEM model was tested by taking into account various compliance criteria, and the validity and reliability of the model were determined.     

Enhancing Blockchain Security Using Ripple Consensus Algorithm

In the development of technology in various fields like big data analysis, data mining, big data, cloud computing, and blockchain technology, security become more constrained. Blockchain is used in providing security by encrypting the sharing of information. Blockchain is applied in the peer-to-peer (P2P) network and it has a decentralized ledger. Providing security against unauthorized breaches in the distributed network is required. To detect unauthorized breaches, there are numerous techniques were developed and those techniques are inefficient and have poor data integrity. Hence, a novel technique needs to be implemented to tackle the new breaches in the distributed network. This paper, proposed a hybrid technique of two fish with a ripple consensus algorithm (TF-RC). To improve the detection time and security, this paper uses efficient transmission of data in the distributed network. The experimental analysis of TF-RC by using the metric measures of performance in terms of latency, throughput, energy efficiency and it produced better performance.     

Blockchain-Based SQKD and IDS in Edge Enabled Smart Grid Network

Smart Grid is a power grid that improves flexibility, reliability, and efficiency through smart meters. Due to extensive data exchange over the Internet, the smart grid faces many security challenges that have led to data loss, data compromise, and high power consumption. Moreover, the lack of hardware protection and physical attacks reduce the overall performance of the smart grid network. We proposed the BLIDSE model (Blockchain-based secure quantum key distribution and Intrusion Detection System in Edge Enables Smart Grid Network) to address these issues. The proposed model includes five phases: The first phase is blockchain-based secure user authentication, where all smart meters are first registered in the blockchain, and then the blockchain generates a secret key. The blockchain verifies the user ID and the secret key during authentication matches the one authorized to access the network. The secret key is shared during transmission through secure quantum key distribution (SQKD). The second phase is the lightweight data encryption, for which we use a lightweight symmetric encryption algorithm, named Camellia. The third phase is the multi-constraint-based edge selection; the data are transmitted to the control center through the edge server, which is also authenticated by blockchain to enhance the security during the data transmission. We proposed a perfect matching algorithm for selecting the optimal edge. The fourth phase is a dual intrusion detection system which acts as a firewall used to drop irrelevant packets, and data packets are classified into normal, physical errors and attacks, which is done by Double Deep Q Network (DDQN). The last phase is optimal user privacy management. In this phase, smart meter updates and revocations are done, for which we proposed Forensic based Investigation Optimization (FBI), which improves the security of the smart grid network. The simulation is performed using network simulator NS3.26, which evaluates the performance in terms of computational complexity, accuracy, false detection, and false alarm rate. The proposed BLIDSE model effectively mitigates cyber-attacks, thereby contributing to improved security in the network.     

A Scalable Double-Chain Storage Module for Blockchain

With the growing maturity of blockchain technology, its peer-to-peer model and fully duplicated data storage pattern enable blockchain to act as a distributed ledger in untrustworthy environments. Blockchain storage has also become a research hotspot in industry, finance, and academia due to its security, and its unique data storage management model is gradually becoming a key technology to play its value in various fields’ applications. However, with the increasing amount of data written into the blockchain, the blockchain system faces many problems in its actual implementation of the application, such as high storage space occupation, low data flexibility and availability, low retrieval efficiency, poor scalability, etc. To improve the above problems, this paper combines off-chain storage technology and de-duplication technology to optimize the blockchain storage model. Firstly, this paper adopts the double-chain model to reduce the data storage of the major chain system, which stores a small amount of primary data and supervises the vice chain through an Application Programming Interface (API). The vice chain stores a large number of copies of data as well as non-transactional data. Our model divides the vice chain storage system into two layers, including a storage layer and a processing layer. In the processing layer, deduplication technology is applied to reduce the redundancy of vice chain data. Our double-chain storage model with high scalability enhances data flexibility, is more suitable as a distributed storage system, and performs well in data retrieval.     

EIAS: An Efficient Identity-Based Aggregate Signature Scheme for WSNs Against Coalition Attack

Wireless sensor networks (WSNs) are the major contributors to big data acquisition. The authenticity and integrity of the data are two most important basic requirements for various services based on big data. Data aggregation is a promising method to decrease operation cost for resource-constrained WSNs. However, the process of data acquisitions in WSNs are in open environments, data aggregation is vulnerable to more special security attacks with hiding feature and subjective fraudulence, such as coalition attack. Aimed to provide data authenticity and integrity protection for WSNs, an efficient and secure identity-based aggregate signature scheme (EIAS) is proposed in this paper. Rigorous security proof shows that our proposed scheme can be secure against all kinds of attacks. The performance comparisons shows EIAS has clear advantages in term of computation cost and communication cost when compared with similar data aggregation scheme for WSNs.