Trusted Data Sharing in Federated and Dynamic Mission Contexts: Improving Communication Flexibility with Emerging Data Control Architectures and Concepts

The emergence of the Internet of Vehicles (IoV) aims to facilitate the next generation of intelligent transportation system (ITS) applications by combining smart vehicles and the internet to improve traffic safety and efficiency. On the other hand, mobile edge computing (MEC) technology provides enormous storage resources with powerful computing on the edge networks. Hence, the idea of IoV edge computing (IoVEC) networks has grown to be an assuring paradigm with various opportunities to advance massive data storage, data sharing, and computing processing close to vehicles. However, the participant’s vehicle may be unwilling to share their data since the data-sharing system still relies on a centralized server approach with the potential risk of data leakage and privacy security. In addition, vehicles have difficulty evaluating the credibility of the messages they received because of untrusted environments. To address these challenges, we propose consortium blockchain and smart contracts to accomplish a decentralized trusted data sharing management system in IoVEC. This system allows vehicles to validate the credibility of messages from their neighboring by generating a reputation rating. Moreover, the incentive mechanism is utilized to trigger the vehicles to store and share their data honestly; thus, they will obtain certain rewards from the system. Simulation results substantially display an efficient network performance along with forming an appropriate incentive model to reach a decentralized trusted data sharing management of IoVEC networks.

Download Full-text

Towards a Trusted and Unified Consortium-Blockchain-Based Data Sharing Infrastructure for Open Learning—TolFob Architecture and Implementation

Sustainability ◽

10.3390/su132414069 ◽

2021 ◽

Vol 13 (24) ◽

pp. 14069

Author(s):

Jun Xiao ◽

Yi Jiao ◽

Yin Li ◽

Zhujun Jiang

Keyword(s):

Information Systems ◽

Data Sharing ◽

Educational Institutions ◽

Production Environment ◽

Open Learning ◽

Integration Framework ◽

Heterogeneous Information ◽

Management Application ◽

Trusted Data ◽

Test Result

Open learning is now facing a complex higher education ecosystem that involves a variety of heterogeneous information systems and comprises decentralized stakeholders, such as universities, professors, students, and software vendors. Authentic, non-repudiable, and fast available data sharing among open learning information systems and stakeholders is a key issue that remains unresolved. To solve this problem, this paper proposes a consortium blockchain extended architecture featuring integration and cross-chain functions to provide a unified and trusted data-sharing infrastructure for open learning. The overall architecture consists of three elements: a blockchain-integrated open learning scenario schema; a blockchain-integrated open learning application model; and a pragmatic blockchain integration framework. The proposed blockchain integration framework is implemented based on Hyperledger Fabric 1.4. A trusted open-learning behavior and achievement management application is developed as a proof-of-concept which integrates two educational institutions’ four productional learning systems into a blockchain network and has stably run over six months. A suite of experiments is designed and executed to verify our blockchain system’s viability and scalability. The test result shows the implementation of the blockchain system is competent for the production environment and outperforms related works investigated. However, it does have limitations and optimization potential, which will be studied in the future.

Download Full-text

Blockchain-Empowered Big Data Sharing for Internet of Things

International Journal of Web Services Research ◽

10.4018/ijwsr.2021010104 ◽

2021 ◽

Vol 18 (1) ◽

pp. 58-69

Author(s):

Ting Cai ◽

Yuxin Wu ◽

Hui Lin ◽

Yu Cai

Keyword(s):

Internet Of Things ◽

Social Welfare ◽

Data Sharing ◽

Large Scale ◽

Data Control ◽

Main Challenge ◽

The Social ◽

Secure Data ◽

Iot Devices ◽

Control And Management

A recent study predicts that by 2025, up to 75 billion internet of things (IoT) devices will be connected to the internet, in which data sharing is increasingly needed by massive IoT applications as a major driver of the IoT market. However, how to meet the interests of all participants in complex multi-party interactive data sharing while providing secure data control and management is the main challenge in building an IoT data sharing ecosystem. In this article, the authors propose a blockchain-empowered data sharing architecture that supports secure data monitoring and manageability in complex multi-party interactions of IoT systems. First, to build trust among different data sharing parties, the authors apply blockchain technologies to IoT data sharing. In particular, on-chain/off-chain collaboration and sharding consensus process are used to improve the efficiency and scalability of the large-scale blockchain-empowered data sharing systems. In order to encourage IoT parties to actively participate in the construction of shared ecology, the authors use an iterative double auction mechanism in the proposed architecture to maximize the social welfare of all parties as a case-study. Finally, simulation results show that the proposed incentive algorithm can optimize data allocations for each party and maximize the social welfare while protecting the privacy of all parties.

Download Full-text

Terrorism and Counterterrorism

The Oxford Handbook of Terrorism ◽

10.1093/oxfordhb/9780198732914.013.38 ◽

2019 ◽

pp. 639-658

Author(s):

Juliette Bird

Keyword(s):

Civil Society ◽

Data Sharing ◽

Small Groups ◽

Terrorist Attacks ◽

Top Down ◽

Regional Organizations ◽

New Thinking ◽

Underlying Causes ◽

Trusted Data

Terrorists force us to change long-established lifestyles. The reactions of civil society, nations, regional organizations and the UN constitute a broad-ranging counterterrorism effort. Relations between civil groups and governments can be tricky for both sides but are essential; nations must provide public reassurance, avoid alienating society or reinforcing stereotypes, and tackle both terrorist attacks and their underlying causes. Small groups of nations or regional organizations struggle to avoid duplication and ensure coordination. Practical results can be hard to assess. The UN, burdened by multitudinous bodies and relationships, finds implementation uphill work. Promisingly, the bottom-up (civil society) approach is now meeting the global top-down (UN) drive. The future should bring not only incremental improvements but new thinking to meet long-term challenges including trusted data-sharing, metrics for projects, matching needs and offers of support and, importantly, societal awareness of the deeper issues surrounding terrorism.

Download Full-text

A Methodology of Trusted Data Sharing across Telecom and Finance Sector under China’s Data Security Policy

10.1109/bigdata52589.2021.9671857 ◽

2021 ◽

Author(s):

Yuxin Liang ◽

Zhiyong Liu ◽

Yong Song ◽

Aidong Yang ◽

Xiaozhou Ye ◽

...

Keyword(s):

Data Sharing ◽

Data Security ◽

Security Policy ◽

Trusted Data

Download Full-text

A Modular Task Orientated Approach for the Analysis of Large Datasets

10.31219/osf.io/ys2vw ◽

2019 ◽

Author(s):

Paul John Palmer ◽

Michael J. de C Henshaw ◽

Russell Lock

Keyword(s):

Data Sharing ◽

Academic Research ◽

Secondary Data ◽

Large Data ◽

Large Datasets ◽

Wide Range ◽

Source Data ◽

Trusted Data ◽

Downstream Analysis ◽

Social Applications

We introduce a novel conceptual framework using task orientated templates, for the analysis of large data that effectively separates: data curation, analysis, and reporting of large datasets, creating a reproducible analysis. Outputs saved include calculated secondary data, with associated metadata capturing all the transformations that have been applied, to provide an auditable connection to the source data. Data sharing is encouraged by many research funders and academic publishers, but supplying provenance is not mandatory. Enhancing data sharing will benefit many sectors as the pool of trusted data increases and is used with confidence in downstream analysis. While such benefits are likely to initially impact academic research due to active encouragement of data sharing, business intelligence processes will also benefit through increased confidence of source data. Using task orientated templates will allow a more structured approach to data analysis, and facilitate reuse of data through the use of verifiable digital signatures. This template based approach will reduce the programmatic skills required for the analysis large data for a wide range of commercial, academic and social applications on desktop computers.

Download Full-text

Survey and analysis of cloud data management and security

ACCENTS Transactions on Information Security ◽

10.19101/tis.2020.517007 ◽

2020 ◽

Vol 5 (17) ◽

pp. 6-10

Author(s):

Md. Farooque ◽

Kailash Patidar ◽

Rishi Kushwah ◽

Gaurav Saxena

Keyword(s):

Cloud Computing ◽

Data Management ◽

Data Sharing ◽

Meta Analysis ◽

Computing Environment ◽

Cloud Computing Environment ◽

Cloud Data ◽

Data Control ◽

Traditional Approaches ◽

Cloud Data Management

This paper explores different security aspects in cloud computing environment. It includes data sharing mechanism, inter cloud communication, data breaches, data control, user-cloud relationship along with the cloud data management with standard security algorithms. It also covers the related reviews and analytical analysis on the traditional approaches for the gap identification. So, a short meta-analysis has been presented based on the method discussed along with the advantages and challenges found. It also explores the future prospective where there is the need of exploration and research.

Download Full-text