Data storage and sharing for the internet of vehicles and GPBFT consensus algorithm

<span lang="EN-US">Cloud Computing is the ability to improve the utility or train new human resources without investing in new infrastructure, or add capabilities to existence without the latest software licensing. It expanded the capabilities of Information Technology (IT). From the past few years, cloud computing has developed from a good business concept in the best rising sectors of the IT industry. But more information on individuals and companies was put in the cloud, and concerns began to think about how secure the cloud environment was. Despite cloud surrounding structures, enterprise users still do not want to expand their business in the cloud. Security reduces the growth of cloud computing and continues to spread the market with complexity with data privacy and data protection. The security of cloud computing has constantly been an significant aspect of improved quality of service from cloud service providers. Data storage in the cloud has a problem related to data security. However, cloud computing construct many new security challenges which have not been well examine. In order to ensure that the user's data in the cloud is secure, we have proposed an effective mechanism with a distinctive feature of data integrity and privacy. This paper focusing on problems relating to the cloud data storage techniques and security in virtual environment. We recommend a method for providing data storage and security in cloud using public key Cryptosystem, which uses the concept of the modified RSA algorithm to provide better security for the data stored in the cloud. </span>

Efficient secure data storage based on novel blockchain model over IoT-based smart computing systems

With the increasing deployment of IoT systems, secure and efficient data storage has become a critical challenge. This paper proposes a multi-model secure redundant storage approach for IoT data by integrating Random Forest (RF), Deep Neural Network (DNN), and Adaptive Particle Swarm Optimization (APSO), refers to the complete proposed system that integrates Random Forest (RF) for feature extraction, Deep Neural Network (DNN) for anomaly detection and sensitivity classification, and Adaptive Particle Swarm Optimization (APSO) for dynamic storage strategy adjustment. The RF extracts key features from high-dimensional data, DNN detects and classifies anomalies, and APSO dynamically adjusts storage parameters for optimized redundancy. The model was evaluated on the SmartHomeIoTData-v1.0 dataset, comprising 1,000 devices and over 1,000,000 data entries across temperature, humidity, and status metrics. Compared to baseline models (KNN, SVM), our approach improves accuracy from 90% to 95%, increases storage resource utilization to 75%, and reduces data loss probability to 0.01%. These results demonstrate enhanced system security, efficiency, and responsiveness on resource-constrained devices.

We describe a novel approach for building a secure and fault tolerant data storage service in collaborative work environments, which uses perfect secret sharing schemes to store data. Perfect secret sharing schemes have found little use in managing generic data because of the high computation overheads incurred by such schemes. Our proposed approach uses a novel combination of XOR secret sharing and replication mechanisms, which drastically reduce the computation overheads and achieve speeds comparable to standard encryption schemes. The combination of secret sharing and replication manifests itself as an architectural framework, which has the attractive property that its dimension can be varied to exploit tradeoffs amongst different performance metrics. We evaluate the properties and performance of the proposed framework and show that the combination of perfect secret sharing and replication can be used to build efficient fault-tolerant and secure distributed data storage systems.

Digital data encoding is crucial for communication and data storage, but conventional techniques, such as ASCII and binary coding, have drawbacks in terms of processing speed and storage capacity. A potential substitute with parallel processing and high-capacity storage is DNA-based data encoding. The goal of this research is to develop a digital data encoding technique based on DNA, while considering biological constraints such as homopolymer and GC-content. The process involves converting image pixel values into binary format, followed by encoding into DNA sequences, ensuring they meet biological constraints. The validity of the resulting DNA sequences is assessed through transcription and translation processes. Additionally, Multiple Sequence Alignment analysis is conducted to compare the similarities between the encoded DNA sequences. The results indicate that the DNA sequences from MNIST images share similar characteristics, reflected in the phylogenetic tree's close clustering. Multiple Sequence Alignment analysis shows that biological constraints successfully preserved the core visual features, allowing accurate clustering. However, this method also faces drawbacks, particularly in the reduction of visual information and sensitivity to changes in image intensity. Despite these challenges, DNA-based encoding shows potential for digital image representation. Further development, particularly the integration of deep learning, could lead to more efficient, secure, and sustainable data storage systems, especially for image data.

For decades the researchers were trying to provide computing as a utility, where the consumer will enjoy the on demand applications and resources from a collection of shared computing resources. This dream comes true in the shape of cloud computing, and the owners of the data now outsource their data to cloud data center. It not only relieves the owner from the extra pain of local storage and maintenance but it allows accessing the data anywhere, anytime on demand. Outsourcing data eliminates the control on the physical computing resources for the owner, and it also creates a chaos among the customer regarding the confidentiality and integrity of their data. The security issues can be handled in numerous ways, i.e efficient access control and authorization mechanism etc. The reliability or accuracy of data is a problem, where the unauthorized changes made to the data without the permission and knowledge of the owner of the data and it is an important component of cloud computing to provide an efficient and secure data storage. Therefore to facilitate the swift employment of data storage service and recapture safety measures for the outsourced data, we propose a set of operational algorithms to resolve the challenge of secure data storage and make the cloud storage service a reality.

The existing quality supervision system of agricultural products is facing challenges such as quality inspection data fraud, data privacy disclosure, and low credibility. In order to solve these problems, this paper designs and implements a Secure Edge-Cloud-BlockChain Integrated Agricultural Product Safety Supervision System based on blockchain and cryptography technology (SECBCAPSS), which enables secure, reliable, and efficient storage of detection data as well as trustworthy traceability. First, quality inspection entities and regulatory entities are deployed as blockchain nodes to achieve transparent supervision and reliable traceability of agricultural product data. Then, a two-stage encryption algorithm is designed and implemented: the SM4 algorithm is used to protect the key data of the inspection record, and the encryption scheme based on the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is used to protect the SM4 key and the detection result certificate. Then, a reliable agricultural product quality detection data storage mechanism is designed to ensure the consistency between cloud storage data and blockchain storage data. The experimental results show that the system function and performance meet the actual needs, can resist malicious data access attacks, and effectively reduce the data storage burden of the blockchain.

Business processes are by nature information-intensive and require IT support. Database systems solve the basic need for secure and efficient data storage and access. As such they are well-understood and widely applied. Workflow management systems, currently being added to support and optimize formal business processes, distribute jobs among employees. Recently, researchers and practioners started to promote the idea to explicitly represent the knowledge of the enterprise as so-called “Organizational Memory”.

In the era of fintech services computing, the convergence of advanced technologies has become pivotal for driving innovation. The seamless integration of IIoT-enabled data streams with artificial intelligence (AI) systems presents unprecedented opportunities for revolutionizing financial risk assessment. Efficient AI-to-AI business platforms are crucial for sustainable and transparent fintech credit risk analysis. However, current practices rely on single AI models, leading to inefficiencies and regulatory challenges. This paper proposes a novel Blockchain Regulatory Framework to address these issues, particularly in lending to small and micro enterprises. The key novelty of our work, compared to existing state-of-the-art, is fourfold: 1) a multi-level guarantee strategy that combines blockchain with AI-AI models to enable distributed risk-sharing among guarantee agencies; 2) a dual-chain blockchain structure for secure and efficient storage of credit data and processes; 3) the integration of Intel SGX with Oblivious RAM (ORAM) to ensure unprecedented confidentiality, integrity, and availability in credit data sharing; and 4) the incorporation of game theory to mathematically validate the anti-collusion properties of the AI-AI decision mechanism. Our findings indicate that the proposed framework offers a comprehensive solution for achieving safe, efficient, and transparent credit assessments. This approach holds significant promise for developing effective financing solutions while ensuring data security, regulatory compliance, and robust credit evaluation methodologies.

With the ongoing advancement of information technology, there has been a growing focus on the security issue of information transmission and storage. Ensuring the secure transmission and efficient storage of data in the digital age has emerged as a critical issue. This article explores innovative solutions based on blockchain technology, aiming to enhance the security and reliability of data management systems. Such systems can accelerate work efficiency across various sectors, safeguard the security of data applications, ensure the rationality of advanced data utilization, and offer fresh insights for the sustainable development and construction of our digital society.

In the current digital era, people's need for data storage media that is practical and can be accessed at any time is increasing. This research aims to design and implement a practical cloud-based data storage system using a Raspberry Pi 4 Model B device using the Samba and OpenVPN applications. The system focuses on storing users' data (private cloud), which allows users to directly access files and data via a storage server. The method used in this research includes a literature review to support system development. Testing was carried out to evaluate the security of the system being built by comparing access to private cloud server services before and after using the OpenVPN application. Test results show that using the OpenVPN application increases the security of data exchange, with good encryption in communications between client and server. The resulting system runs according to the initial design and can function as a secure private cloud system. This research can contribute to the development of efficient and secure data storage solutions, as well as show the potential for using the Raspberry Pi as an energy and cost-saving personal cloud server device.

The application of blockchain technology in the medical information system is gradually triggering profound changes, which has heterogeneous, cross-domain, and open network characteristics. However, today’s electronic medical systems fall short of ensuring the confidentiality and safe sharing of medical data, which poses serious threats to their authenticity and accuracy. Moreover, most of the current blockchain medical systems are under great transmission and storage pressure. Based on the aforementioned security and performance considerations, this paper proposes a fine-grained secure sharing scheme of medical data based on blockchain (B-SSMD). We design a three-chain model to store patient information, medical staff information, and medical records hierarchically. The integration of IPFS technology and the encryption algorithm ensures secure and efficient off-chain data storage. The user classification is realized by assigning attributes, and the attribute encryption technology is adopted for secondary encryption of the key and ciphertext path. Meanwhile, through hierarchical encryption, the risk of a system attack is greatly reduced. Our scheme not only solves the problems of low throughput and poor stability in the single chain model but also improves the data confidentiality and enables medical data to be managed more safely and efficiently in the sharing process. We provide the security and performance analysis and it is confirmed that our scheme has higher security and controllability.

As the volume of digital data continues to grow at an unprecedented rate, the demand for secure, efficient, and scalable data storage solutions is more critical than ever. The fast-paced evolution of digital technologies has led to a surge in data gen

The Creative Class Information System is designed to simplify class management activities, including recording data for mentors, affiliations, materials, suppliers, schedules, venues, and participants. This integrated system features payment and material purchase transactions, streamlining administrative processes. With an organized database structure, the system ensures efficient and secure data storage. The web-based platform allows broader and more flexible access for users, including admins, mentors, and participants. The system's development aims to enhance class operational efficiency and provide a better experience for all stakeholders involved.

Aiming at the security issues about isolation storage, access, and sharing of the multi-tenant data under cloud environment, based on the Hadoop storage architecture, this paper proposes a Tag-Based protection method for Multi-tenant data security. For the isolation storage and access security of the private data, by introducing security tag to participate in storage and authentication access of the data, a consistent hash improved algorithm on dual authentication of dynamic password and security tag is proposed, realizing efficient isolation storage and secure access of the multi-tenant private data. For the security of the sharing data, a tag-based proxy re-encryption data sharing scheme is proposed, completing data secret sharing under the security authentication of the tenant sharing tag. For proxy re-encryption, use sharing tag to replace sharing data. Through the analysis of the security and instantiation of the method, this method is proved to be reliable and feasible under the cloud environment.