Secure Data Sharing Research Articles

Cloud-Based Blockchain for Secure Data Sharing and Collaboration

Abstract: This study seeks to delve into the interrelation between user training and data security events in light of cloud-based blockchain technology improvement in terms of data security in collaborative contexts. A total of five firms was taken to collect data from a quantitative study, focusing more on variables that surrounded user training levels as well as data security events. This reflects that there exists a strong statistical negative correlation of frequency of security incidents and user training. Hence it has been established that the increase in the extent of training has considerably reduced vulnerabilities. Moreover, it was observed that there exists a positive relation between user participation and training. This means that to safe-guard data shared procedures, successful training activities can play an essential role. Such findings may be practically useful for firms who wish to take maximum advantage of their data security strategy.

Secure Data Sharing Scheme for Multi-Node Devices

For the problem that the communication between maritime devices may be listened to by the adversary, resulting in the data shared between maritime devices may also be tampered with and forged by the adversary, thus jeopardizing the maritime IoT, this paper firstly starts from the wireless sensor network technology which is the most widely used in the maritime IoT, and then selects and designs a lightweight block cipher algorithm for realizing the secure data sharing of the multi-node devices in the maritime IoT. After analyzing the security of the algorithm, it is concluded that this paper's scheme performs well in terms of anti-differential analysis and linear analysis, and is better than PRESENT algorithm in terms of hardware implementation.

Secure Data Sharing in Cloud Computing: A Comprehensive Survey of Two-Factor Authentication and Cryptographic Solutions

Secure Data Sharing in Cloud Computing: A Comprehensive Survey of Two-Factor Authentication and Cryptographic Solutions

Secure Data Sharing in a Cyber-Physical Cloud Environment

Cloud computing plays a significant role in digital workplaces and has become an integral part of everyday life. However, the security issues associated with cloud computing remain a major concern that hinders the wider adoption of this technology. In a cyber-physical cloud environment, achieving secure and efficient file storage remains a tough goal. This is particularly the case owing to the wide variety of devices that are being utilised to access the various services and data. Thus, the employment of a secure data sharing protocol is one of the essential techniques to better protect the shared data. A formal agreement between entities or organisations in exchanging personal or business data is called a data sharing protocol. A secure data sharing protocol ensures that data is encrypted and secured when being transferred. There are many different encryption algorithms and protocols in use to devise various secure and efficient data sharing protocols. This paper first reviews the state of the art of some existing data sharing protocols and subsequently implements a secure and efficient data sharing protocol as an application in a cyber-physical cloud environment. The performance analysis conducted on the developed secure data sharing protocol application shows positive results.

SparkAC: Fine-Grained Access Control in Spark for Secure Data Sharing and Analytics

With the development of computing and communication technologies, an extremely large amount of data has been collected, stored, utilized, and shared, while new security and privacy challenges arise. Existing access control mechanisms provided by big data platforms have limitations in granularity and expressiveness. In this article, we present SparkAC, a novel access control mechanism for secure data sharing and analysis in Spark. In particular, we first propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">purpose-aware access control</i> (PAAC) model, which introduces new concepts of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data processing purpose</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data operation purpose</i> and an automatic purpose analysis algorithm that identifies purposes from data analytics operations and queries. Moreover, we develop a unified access control mechanism that implements PAAC model in two modules. GuardSpark++ supports structured data access control in Spark Catalyst and GuardDAG supports unstructured data access control in Spark core. Finally, we evaluate GuardSpark++ and GuardDAG with multiple data sources, applications, and data analytics engines. Experimental results show that SparkAC provides effective access control functionalities with very small (GuardSpark++) or medium (GuardDAG) performance overhead.

Secure Data Sharing in Smart Homes: An Efficient Approach Based on Local Differential Privacy and Randomized Responses

Secure Data Sharing in Smart Homes: An Efficient Approach Based on Local Differential Privacy and Randomized Responses

Medi-Block Record Secure Data Sharing in Healthcare System: Issues, Solutions and Challenges

Medi-Block Record Secure Data Sharing in Healthcare System: Issues, Solutions and Challenges

Secure Data Sharing in IoT Networks using Blockchain and Machine Learning

The burgeoning proliferation of IoT networks has underscored the pressing need for robust and secure data sharing mechanisms. The existing methods for data sharing in IoT networks exhibit certain limitations, notably in terms of energy efficiency, speed, throughput, packet delivery ratio, and overall consistency. These limitations have sparked the demand for innovative solutions that can mitigate these issues effectively. To address these limitations, the paper proposes an advanced model that harnesses the power of Analytic Hierarchy Process (AHP) based Smart Contracts, fortified with Genetic Algorithm optimized Sidechains, within the blockchain framework. This integration synergizes the strengths of blockchain technology and machine learning, offering a robust foundation for secure data sharing in IoT networks. The advantages of this approach are manifold. By leveraging AHP and Smart Contracts, the model ensures the precision and reliability of data transactions. The incorporation of Genetic Algorithm optimized Sidechains optimizes the scalability and efficiency of the system. Consequently, the proposed model exhibits a remarkable 4.9% improvement in energy efficiency, a 5.5% boost in speed, an 8.3% increase in throughput, an 8.5% enhancement in packet delivery ratio, and a 3.9% better consistency compared to existing methods. The impacts of this work are profound. It not only addresses the current limitations of IoT data sharing but also paves the way for more efficient, secure, and reliable data exchange in IoT networks. This innovation holds the potential to revolutionize the IoT landscape, offering a robust solution that can unlock new possibilities for a wide range of applications, from smart cities to industrial automation sets.

SDSM: Secure Data Sharing for Multilevel Partnerships in IoT Based Supply Chain

Symmetric encryption algorithms enable rapid encryption of data in IoT based supply chains, which helps to alleviate the concerns of supply chain participants about privacy disclosure when sharing data. However, in supply chain management where multilevel partnerships exist universally, a pure symmetric encryption scheme cannot provide efficient data sharing and fine-grained access control. To overcome these problems, this paper proposes a secure data sharing scheme (SDSM) for IoT based supply chains by combining blockchain and ciphertext-based attribute cryptography. This scheme supports the enforcement of fine-grained access control for different levels of partnerships. In addition, to identify partnerships, we propose a metric based on the historical transaction facts on the blockchain, where the level of partnerships among participants is automatically calculated by smart contracts. Finally, we introduce personalized attributes of participants in the ciphertext-based attribute encryption algorithm to support the construction of access policies that include partnerships, allowing for more fine-grained access control. Security analyses and simulation experiments show that our proposed scheme is secure, effective, and practical.

The Trend of Metaverse and Augmented & Virtual Reality Extending to the Healthcare System.

There is no escaping Internet's favorite buzzword for 2022: The Metaverse. Everyone is talking about it, but only a few know what it is or how it works. One can look at the Metaverse as a 3D model of the Internet where it is possible to spend your reality parallel to the virtual world. In broad terms, Metaverse can be explained as a virtual space, graphically rich, leaning towards verisimilitude where people can do all sorts of things they do in real-life such as shop, play, socialize, and party. The pandemic has accelerated innovations in the digital age. Looking beyond revolutions in telehealth, payments, remote monitoring, and secure data-sharing are other essential innovations in the fields of artificial intelligence (AI), virtual reality (VR), augmented reality (AR), and blockchain technology. The Metaverse is still in its nascent stage and evolving continuously, having a huge potential in health care to combine the technologies of AI, AR/VR, web 3.0, Internet of medical devices, and quantum computing, along with robotics to give a new direction to healthcare systems. From improving surgical precision to therapeutic usage and more, the Metaverse can bring significant changes to the industry

Blockchain Based Framework for Secure Data Sharing of Medicine Supply Chain in Health Care System

The potential for blockchain technology in the healthcare sector is summarize in this study. It covers a wide range of technology themes, including storing medical information in blockchains, patient personal data ownership, and patient outreach via mobile apps. Blockchain was created to safeguard digital money transactions, but it has since gained popularity in a variety of other industries, including tourism, real estate, voting, the stock market, and supply chain management. In the healthcare industry, blockchain technology is rapidly gaining traction. Threats to integrity as well as threats to threats to threats to threats to threats to threats to Data management and medicine traceability are two of the most well-known blockchain uses in healthcare. We've explored the problems with standard data processing and drug tracing approaches in this paper.

DSAS: A Secure Data Sharing and Authorized Searchable Framework for e-Healthcare System

In e-healthcare system, an increasing number of patients enjoy high-quality medical services by sharing encrypted personal healthcare records (PHRs) with doctors or medical research institutions. However, one of the important issues is that the encrypted PHRs prevent effective search of information, resulting in the decrease of data usage. Another issue is that medical treatment process requires the doctor to be online all the time, which may be unaffordable for all doctors (e.g., to be absent under certain circumstances). In this paper, we design a new secure and practical proxy searchable re-encryption scheme, allowing medical service providers to achieve remote PHRs monitoring and research safely and efficiently. Through our scheme DSAS, (1) patients’ healthcare records collected by the devices are encrypted before uploading to the cloud server ensuring privacy and confidentiality of PHRs; (2) only authorized doctors or research institutions have access to the PHRs; (3) Alice (doctor-in-charge) is able to delegate medical research and utilization to Bob (doctor-in-agent) or certain research institution through the cloud server, supporting minimizing information exposure to the cloud server. We formalize the security definition and prove the security of our scheme. Finally, performance evaluation shows the efficiency of our scheme.

Secure Data Storage and Sharing Techniques for Data Protection in Cloud Environments: A Systematic Review, Analysis, and Future Directions

A large number of researchers, academia, government sectors, and business enterprises are adopting the cloud environment due to the least upfront capital investment, maximum scalability, and several other features of it. Despite the multiple features supported by the cloud environment, it also suffers several challenges. Data protection is the primary concern in the area of information security and cloud computing. Numerous solutions have been developed to address this challenge. However, there is a lack of comprehensive analysis among the existing solutions and a necessity emerges to explore, classify, and analyze the significant existing work for investigating the applicability of these solutions to meet the requirements. This article presents a comparative and systematic study, and in-depth analysis of leading techniques for secure sharing and protecting the data in the cloud environment. The discussion about each dedicated technique includes: functioning for protecting the data, potential and revolutionary solutions in the domain, the core and adequate information including workflow, achievements, scope, gaps, future directions, etc. about each solution. Furthermore, a comprehensive and comparative analysis of the discussed techniques is presented and the research gaps and future directions are reported in the field. The authors believe that this article&#x2019;s contribution will operate as a catalyst for the potential researchers to carry out the research work in the area

Building a Trust Model for Secure Data Sharing (TM-SDS) in Edge Computing Using HMAC Techniques

With the rapid growth of Internet of Things (IoT) based models, and the lack amount of data makes cloud computing resources insufficient. Hence, edge computing-based techniques are becoming more popular in present research domains that makes data storage, and processing effective at the network edges. There are several advanced features like parallel processing and data perception are available in edge computing. Still, there are some challenges in providing privacy and data security over networks. To solve the security issues in Edge Computing, Hash-based Message Authentication Code (HMAC) algorithm is used to provide solutions for preserving data from various attacks that happens with the distributed network nature. This paper proposed a Trust Model for Secure Data Sharing (TM-SDS) with HMAC algorithm. Here, data security is ensured with local and global trust levels with the centralized processing of cloud and by conserving resources effectively. Further, the proposed model achieved 84.25% of packet delivery ratio which is better compared to existing models in the resulting phase. The data packets are securely transmitted between entities in the proposed model and results showed that proposed TM-SDS model outperforms the existing models in an efficient manner.

LVBS: Lightweight Vehicular Blockchain for Secure Data Sharing in Disaster Rescue

In disaster areas, a large amount of data (e.g., rescue commands, road damage, and rescue experience) should be delivered among ground rescuing vehicles for safe driving and efficient rescue. When communication infrastructures are destroyed by disasters, unmanned aerial vehicles (UAVs) can be employed to perform immediate rescue missions in destroyed areas and assist data sharing for ground Internet of vehicles (IoV). However, in such UAV-assisted IoV under disaster situation, there exist potential security threats on data sharing among vehicles and UAVs because of the untrusted network environment, unreliable misbehavior tracing, and low-quality shared data. To address these issues, in this article, we develop a <u>l</u>ightweight <u>v</u>ehicular <u>b</u>lockchain-enabled <u>s</u>ecure (LVBS) data sharing framework in UAV-aided IoV for disaster rescue. First, we propose a novel UAV and blockchain-assisted collaborative aerial-ground network architecture in disaster areas. Second, we develop a credit-based consensus algorithm in the lightweight vehicular blockchain to securely and immutably trace misbehaviors and record data transactions for UAVs and vehicles with improved efficiency and security in reaching consensus. Third, since UAVs and vehicles have little explicit knowledge of the whole network, we develop reinforcement learning-based algorithms to optimally schedule the pricing and quality of data sharing strategies for both data contributor and data consumer via trial and error. Finally, extensive simulations are conducted, which demonstrate that LVBS can effectively improve the security of consensus phase and promote high-quality data sharing.

Computer Prediction Model for Equipment Maintenance Using Cloud Computing and Secure Data-sharing

A With the popularization of automation in the industrial field, productivity has been greatly improved. However, manufacturing corporations are facing a data tsunami which brings new challenges to predictive maintenance (PdM). In recent years, many approaches and architecture for predictive maintenance have been proposed to solve some of these problems to varying degrees. This paper introduces a general framework based on the Internet of Things, cloud computing and big data analytics for PdM of industrial equipment. In this framework, smart sensors are installed on the device to obtain electrical data, which is then encrypted and uploaded to the cloud platform to predict the health condition by deep learning methods. Several working instances including feature selection, feature fusion, and Remaining Useful Life (RUL) prediction are provided. The effectiveness of the proposed methods is demonstrated by real-world cases.

Secure Data Sharing Platform for Portable Social Networks with Power Saving Operation

Several subscribing and content sharing services are largely personalized with the growing use of mobile social media technology. The end user privacy in terms of social relationships, interests and identities as well as shared content confidentiality are some of the privacy concerns in such services. The content is provided with fine-grained access control with the help of attribute-based encryption (ABE) in existing work. Decryption of privacy preserving content suffers high consumption of energy and data leakage to unauthorized people is faced when mobile social networks share privacy preserving data. In the mobile social networks, a secure proxy decryption model with enhanced publishing and subscribing scheme is presented in this paper as a solution to the aforementioned issues. The user credentials and data confidentiality are protected by access control techniques that work on privacy preserving in a self-contained manner. Keyword search based public-key encryption with ciphertext policy attribute-based encryption is used in this model. At the end users, ciphertext decryption is performed to reduce the energy consumption by the secure proxy decryption scheme. The effectiveness and efficiency of the privacy preservation model is observed from the experimental results.

A Comprehensive Review on Secure Data Sharing in Cloud Environment

A Comprehensive Review on Secure Data Sharing in Cloud Environment

Federated deep reinforcement learning based secure data sharing for Internet of Things

The increasing number of Internet of Things (IoT) devices motivate the data sharing that improves the quality of IoT services. However, data providers usually suffer from the privacy leakage caused by direct data sharing. To solve this problem, in this paper, we propose a Federated Learning based Secure data Sharing mechanism for IoT, named FL2S. Specifically, to accomplish efficient and secure data sharing, a hierarchical asynchronous federated learning (FL) framework is developed based on the sensitive task decomposition. In addition, to improve data sharing quality, the deep reinforcement learning (DRL) technology is utilized to select participants of sufficient computational capabilities and high quality datasets. By integrating task decomposition and participant selection, reliable data sharing is realized by sharing local data models instead of the source data with data privacy preserved. Experiment results show that the proposed FL2S achieves high accuracy in secure data sharing for various IoT applications.

Privacy and Utility-Assisted Data Protection Strategy for Secure Data Sharing and Retrieval in Cloud System

ABSTRACT The outsourcing of Electronic Health Records (EHR) on cloud infrastructures has enabled medical data sharing among several healthcare applications. The blockchain offers security by authenticating users with encryption methods. The collaboration with the cloud provides better management but poses threats to the privacy of the patient. This paper devises a novel blockchain-assisted framework for effective data sharing and retrieval using cloud platforms. Here, the data protection model is devised in EHR application for secure transmission. The entities in the cloud platform include data user, data owner, smart agreement, transactional blockchain, and Inter-Planetary File System (IPFS). Here, the data owner includes a data protection model to secure EHR in which secured EHR is transferred to IPFS before sharing with the data user. The data protection is done by preserving data privacy using Tracy-Singh product and proposed Conditional Autoregressive Value at risk (CAViaR)-based Bird swarm algorithm (CAViaR-based BSA) combination of BSA and CAViaR for generating optimal privacy-preserving coefficients. The objective function is newly devised considering privacy and utility. The proposed CAViaR-based BSA outperformed other methods with minimal responsiveness of 251.339 s, maximal genuine user detection of 32.451%, maximal privacy of 96.5%, and minimal information loss of 3.5%.